eli_wide_self <- clean_data_eli %>%
select(sub_id, eli_number, eli_self, pol_orient_1, pol_orient_2, pol_orient_3) %>%
unique() %>%
pivot_wider(names_from = eli_number, values_from = eli_self) %>%
select(-sub_id) %>%
rename("Politics: Overall" = pol_orient_1,
"Politics: Social" = pol_orient_2,
"Politics: Economic" = pol_orient_3,
"ElI 1" = `1`,
"ElI 2" = `2`,
"ElI 3" = `3`,
"ElI 4" = `4`,
"ElI 5" = `5`,
"ElI 6" = `6`,
"ElI 7" = `7`,
"ElI 8" = `8`,
"ElI 9" = `9`,
"ElI 10" = `10`)
eli_pol_cor <- cor(eli_wide_self)
corrplot(eli_pol_cor,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
Responses on the ELI are not related to responses on the political orientation questions nor each other (aka orthogonal)
eli_wide_targ <- clean_data_eli %>%
select(sub_id, eli_number, eli_targ, pol_orient_1, pol_orient_2, pol_orient_3) %>%
unique() %>%
pivot_wider(names_from = eli_number, values_from = eli_targ) %>%
select(-sub_id) %>%
rename("Politics: Overall" = pol_orient_1,
"Politics: Social" = pol_orient_2,
"Politics: Economic" = pol_orient_3,
"ElI 1" = `1`,
"ElI 2" = `2`,
"ElI 3" = `3`,
"ElI 4" = `4`,
"ElI 5" = `5`,
"ElI 6" = `6`,
"ElI 7" = `7`,
"ElI 8" = `8`,
"ElI 9" = `9`,
"ElI 10" = `10`)
eli_targ_matrix_targ <- cor(eli_wide_targ)
corrplot(eli_targ_matrix_targ,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
The target has slightly higher correlations overall for the ELI, but lower ones in relation to political orientation
eli_wide_stereo <- clean_data_eli %>%
select(sub_id, eli_number, eli_stereo, pol_orient_1, pol_orient_2, pol_orient_3) %>%
unique() %>%
pivot_wider(names_from = eli_number, values_from = eli_stereo) %>%
rename("Politics: Overall" = pol_orient_1,
"Politics: Social" = pol_orient_2,
"Politics: Economic" = pol_orient_3,
"ElI 1" = `1`,
"ElI 2" = `2`,
"ElI 3" = `3`,
"ElI 4" = `4`,
"ElI 5" = `5`,
"ElI 6" = `6`,
"ElI 7" = `7`,
"ElI 8" = `8`,
"ElI 9" = `9`,
"ElI 10" = `10`) %>%
select(-sub_id)
eli_stereo_matrix_stereo <- cor(eli_wide_stereo)
corrplot(eli_stereo_matrix_stereo,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
Again, higher correlations than for the self, but still only .35 as the highest. Low correlations with politics, but higher than with the target.
eli_data <- clean_data_eli %>%
select(sub_id, eli_number, eli_targ_pmc, eli_self_pmc, itt_comp_gmc,
target_condition, eli_targ, eli_self, eli_stereo, eli_stereo_pmc,
analog_condition, itt_comp) %>%
unique() %>%
na.omit()
https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html
eli_randint_test <- lmer(eli_targ_pmc ~ eli_self_pmc + # itt does not work as a RE; model does not converge
(1 | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(eli_randint_test)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc + (1 | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12331.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0103 -0.5900 0.0054 0.6712 3.4945
##
## Random effects:
## Groups Name Variance
## sub_id (Intercept) 0.00000000000000000000000000000001469
## Residual 1.07012291677120230382058707618853077
## Std.Dev.
## 0.0000000000000001212
## 1.0344674556365716089
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.000000000000000006571 0.015886707663029700499 0.000
## eli_self_pmc 0.009365790070278842347 0.012313692726427382870 0.761
##
## Correlation of Fixed Effects:
## (Intr)
## eli_slf_pmc 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
The random variance for the intercept is 0, which is causing the singularity. This does not occur with the BFI. The data looks normal in the descriptives document. Checking some more stuff below.
Person mean centered variables
eli_coeffs_per_sub_c <- lmList(eli_targ_pmc ~ 1 + eli_self_pmc | sub_id, eli_data)
eli_coeffs_per_sub_c
## Call:
## Model: eli_targ_pmc ~ 1 + eli_self_pmc | sub_id
## Data: eli_data
##
## Coefficients:
## (Intercept) eli_self_pmc
## 1 -0.000000000000000267775434043985699176588 -0.27118644067796626728750
## 2 -0.000000000000000243289788017776047509024 0.13281250000000000000000
## 3 -0.000000000000000196857448059479307586189 -0.03571428571428569842539
## 4 -0.000000000000000167319275342577301607952 0.30855018587360610871073
## 5 0.000000000000000089366670109225120070324 0.20454545454545453031514
## 6 0.000000000000000156071112271226281481201 0.22270742358078607359140
## 8 -0.000000000000000155397546969788386177571 -0.49180327868852463657490
## 10 0.000000000000000170140391169486285576620 -0.07692307692307678856913
## 11 0.000000000000000298420844829019646021177 0.75000000000000022204460
## 12 0.000000000000000219427091786043808064706 -0.00000000000000002633125
## 13 0.000000000000000083382294878696653474083 0.12500000000000008326673
## 14 0.000000000000000151743406292845308242439 -0.02684563758389261339810
## 15 0.000000000000000226495884004654588931801 -0.32885906040268447769748
## 16 0.000000000000000040212737955683429208593 0.39007092198581533315505
## 17 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 19 0.000000000000000035108334685767011015986 -0.56250000000000022204460
## 20 -0.000000000000000122879171400184535474464 0.14285714285714287696827
## 21 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 22 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 23 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 25 0.000000000000000014853526213209126919313 -0.19230769230769223510080
## 26 -0.000000000000000181228234680755028013640 -0.23943661971830995649491
## 27 0.000000000000000123971754429824576215232 -0.26556016597510367915902
## 29 0.000000000000000061312381588911945133482 0.07246376811594204547795
## 30 0.000000000000000078098132260175591279845 0.69387755102040815646802
## 32 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 33 0.000000000000000291752982815056070140496 -0.12403100775193799665264
## 34 0.000000000000000175070420614260991107038 -0.28187919463087246496613
## 35 -0.000000000000000089928657949257034011453 -0.10963455149501659391387
## 36 0.000000000000000000000000000000000000000 -0.19047619047619041010577
## 37 -0.000000000000000094577554663698880278274 0.03061224489795920031265
## 38 -0.000000000000000038672184095983290077489 0.07382550335570468857949
## 39 0.000000000000000130044137662585941893699 0.13265306122448983217055
## 40 0.000000000000000035108334685767011015986 0.20689655172413787265207
## 41 0.000000000000000071313804830464238237327 0.21875000000000002775558
## 42 0.000000000000000132663461394578643243994 0.04918032786885227075624
## 43 -0.000000000000000102532295616387739186483 0.05303030303030311504164
## 44 -0.000000000000000121578862708119086741627 -0.26851851851851848973496
## 45 0.000000000000000231301969694465022453051 -0.70588235294117618323639
## 47 0.000000000000000263574512640907555070917 0.08457711442786065847432
## 48 -0.000000000000000149102063356837710500153 -1.04938271604938293535270
## 50 0.000000000000000112716232412199335911331 0.22368421052631584644566
## 51 0.000000000000000165136262017442557518891 -0.04072398190045244809054
## 52 0.000000000000000093622225828712037593264 -0.08333333333333328707404
## 53 -0.000000000000000211300162460634778136383 -0.06481481481481485285023
## 54 -0.000000000000000068490029960758668105048 -0.50819672131147497484704
## 55 0.000000000000000070216669371534022031972 -0.31250000000000005551115
## 58 0.000000000000000351083346857670073182007 0.08888888888888889228124
## 59 0.000000000000000105696520826462583665793 -0.00529100529100527509907
## 60 0.000000000000000238874355607081398286630 0.40196078431372550543443
## 61 0.000000000000000289031406482826062311393 -0.27906976744186057226571
## 62 0.000000000000000130583941681902602061529 -0.42986425339366501896521
## 63 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 64 0.000000000000000114729022276702908247341 0.03571428571428571230317
## 65 0.000000000000000064192162236106521569961 -0.20710059171597619553928
## 66 0.000000000000000083047426224565333197433 -0.34136546184738952858240
## 67 -0.000000000000000120640720530957763958845 -0.16107382550335569426636
## 68 0.000000000000000123886377723136864297350 0.35245901639344262568088
## 69 -0.000000000000000000000000000000010994169 0.00000000000000015657492
## 71 0.000000000000000015517495993709176020489 0.22099447513812156551616
## 72 0.000000000000000111401446599068393346580 -0.05769230769230770244915
## 73 -0.000000000000000065513160418416903961981 0.13397129186602868555767
## 74 -0.000000000000000257969937473679301351840 -0.95652173913043470054873
## 75 0.000000000000000150549299584729719458997 -0.11864406779661021418271
## 76 -0.000000000000000259690221643927400925508 -0.34920634920634929798666
## 78 -0.000000000000000511820300840699664454278 0.71485943775100369013131
## 80 -0.000000000000000140433338743068044063945 -0.33333333333333348136307
## 81 0.000000000000000175541673428835036591003 -0.00000000000000005162432
## 82 -0.000000000000000149210422414509773706782 0.25000000000000011102230
## 83 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 84 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 85 -0.000000000000000312999729571414243839183 -0.43220338983050832215582
## 86 -0.000000000000000334421764294933178659400 -0.44067796610169485127528
## 87 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 88 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 89 0.000000000000000043348045887528643189249 -0.15306122448979597727714
## 90 0.000000000000000028466217312784041998504 -0.10135135135135128758854
## 91 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 92 -0.000000000000000106920837452108604007902 0.27272727272727276259801
## 93 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 94 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 95 0.000000000000000136108398962937322055474 0.40579710144927527704084
## 96 -0.000000000000000101590074835410903463706 0.02127659574468088871102
## 97 0.000000000000000175541673428835036591003 -0.12499999999999997224442
## 98 -0.000000000000000045793480024913481107462 -0.32608695652173907975069
## 99 0.000000000000000000000000000000000000000 -0.16666666666666674068153
## 100 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 101 -0.000000000000000132292275627527865810100 0.05797101449275357532009
## 102 -0.000000000000000142383801781166192511295 -0.05555555555555555941050
## 103 0.000000000000000315975012171903056003045 0.08333333333333346748528
## 104 0.000000000000000000000000000000000000000 0.09708737864077665591367
## 105 0.000000000000000254232768414174897581386 0.20689655172413781714091
## 106 0.000000000000000239216417082524281770600 -0.03726708074534159503832
## 107 -0.000000000000000021522350765221152187796 -0.30651340996168580099734
## 108 0.000000000000000140433338743068044063945 0.22727272727272718189084
## 110 -0.000000000000000005679289434462313460435 -0.11764705882352939625424
## 111 0.000000000000000035108334685767011015986 0.03448275862068969632723
## 112 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 113 0.000000000000000162078519661623521467316 0.09322033898305082111335
## 114 -0.000000000000000121502373961527005872385 0.01960784313725486352475
## 115 -0.000000000000000179442599505031358137608 0.38888888888888883954564
## 116 -0.000000000000000024766344623292988660964 0.57364341085271330911155
## 117 -0.000000000000000183520840402872860575840 -1.45454545454545391969248
## 118 0.000000000000000270882164314919249643612 -0.11640211640211646504373
## 120 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 121 0.000000000000000112414187022696279539545 -0.03846153846153845673461
## 122 0.000000000000000092601178063801564624833 0.29530201342281880982910
## 123 -0.000000000000000269163899257547049532364 -0.22222222222222223764199
## 124 0.000000000000000106851453391464811848322 -0.52173913043478270523678
## 125 0.000000000000000091511888771097616149860 0.09836065573770495784611
## 126 0.000000000000000000000000000000000000000 0.30000000000000009992007
## 127 0.000000000000000040069295021799315228329 0.38043478260869578733150
## 129 -0.000000000000000035399690160337655266788 0.42738589211618271956894
## 130 0.000000000000000207928431782372070592765 -0.24031007751937993877078
## 131 -0.000000000000000021845186026699470918913 0.31111111111111111604544
## 132 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 133 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 134 -0.000000000000000056403554085330574318995 -0.75409836065573787600158
## 135 0.000000000000000084570353165715019018351 0.73480662983425415468730
## 136 0.000000000000000160556408623934500921073 0.47560975609756089843572
## 137 0.000000000000000193002961579428155985282 0.24867724867724863568874
## 138 -0.000000000000000491516685600738141897854 0.00000000000000003204938
## 139 -0.000000000000000293684006022209747762039 0.28042328042328046322496
## 140 0.000000000000000012464497521574082995104 -0.11834319526627216923043
## 141 -0.000000000000000194592707754445017439017 -0.31782945736434098371603
## 142 0.000000000000000115170198440082256924553 0.17989417989417991194934
## 143 -0.000000000000000351083346857670073182007 -0.33333333333333337034077
## 144 -0.000000000000000191110075235193257833887 -0.22171945701357470559500
## 145 0.000000000000000151572127659435890907719 0.06827309236947785853200
## 146 0.000000000000000149653302783252265075071 -0.28440366972477065798941
## 147 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 148 0.000000000000000067408002596672645373475 -0.04000000000000016736612
## 149 -0.000000000000000269593359009054915467935 -0.12844036697247707135006
## 150 0.000000000000000214931512344573642433346 0.12195121951219509148778
## 151 -0.000000000000000202548084725578890270535 -0.23076923076923080979661
## 153 -0.000000000000000053993857417015276764525 0.34831460674157305179577
## 154 0.000000000000000027306482533374367922776 -0.65277777777777767909129
## 155 0.000000000000000146438711781422907821901 -0.34210526315789457907357
## 156 0.000000000000000100173237554498251092967 -0.06521739130434785203239
## 157 -0.000000000000000159132343086574345153612 -0.31884057971014501120521
## 158 -0.000000000000000127409279101573873341378 -0.72580645161290358036155
## 159 -0.000000000000000377089520698978899927309 0.79012345679012319088486
## 160 0.000000000000000222160937519771540118323 -0.06557377049180322658994
## 161 0.000000000000000423098598948761855189301 -0.21311475409836067029801
## 162 -0.000000000000000210650008114602053769965 -0.42857142857142865866038
## 163 0.000000000000000175541673428835036591003 0.50000000000000011102230
## 164 0.000000000000000175541673428835036591003 0.24999999999999997224442
## 165 0.000000000000000136749130782215947538210 -0.04938271604938269943030
## 166 0.000000000000000210504932351437714009939 0.57024793388429750873314
## 168 -0.000000000000000136532412666871673213533 0.11111111111111100779869
## 169 -0.000000000000000113297202326558154961437 -0.25764192139737995823623
## 171 -0.000000000000000198111317155399537069520 -0.35714285714285720629846
## 172 -0.000000000000000008070881536957920514844 0.03831417624521066961352
## 173 0.000000000000000037288976591715263803802 0.03105590062111794613653
## 174 -0.000000000000000130760634288826104722336 0.11224489795918365930838
## 175 -0.000000000000000122879171400184535474464 0.25000000000000011102230
## 176 0.000000000000000147843499547645920005925 -0.22950819672131145265936
## 177 0.000000000000000099473614943006532239123 0.05555555555555555247160
## 178 0.000000000000000113236741451276663909707 0.15492957746478885905361
## 179 -0.000000000000000197797849881419485861663 0.10714285714285719242067
## 180 0.000000000000000187940527685890296465978 0.33828996282527873384893
## 181 -0.000000000000000213702906782929599044742 0.13043478260869570406477
## 182 -0.000000000000000014043333874306809336775 0.40000000000000013322676
## 184 -0.000000000000000214515145878172723930590 0.11009174311926601952383
## 185 0.000000000000000060007862831701062387242 0.17730496453900715381202
## 186 -0.000000000000000046864066661996659352767 -0.20814479638009059092596
## 187 -0.000000000000000115355956824663010663055 0.10714285714285715078731
## 189 -0.000000000000000164570318839532849885553 -0.21875000000000000000000
## 190 -0.000000000000000280866677486136088127889 0.00000000000000005466360
## 191 0.000000000000000280866677486136088127889 -0.14814814814814822319100
## 192 0.000000000000000019391231367138762641531 -0.23255813953488380096957
## 193 -0.000000000000000367244326316197743843449 0.78835978835978837153675
## 194 -0.000000000000000151447718252328280880575 0.53921568627450988664407
## 195 -0.000000000000000117708176368947530459929 0.14728682170542636842292
## 196 -0.000000000000000140433338743068044063945 0.18918918918918914418015
## 198 -0.000000000000000186180562727552299583475 -0.15151515151515149160133
## 199 -0.000000000000000118342701187978693526721 0.67415730337078660916461
## 200 -0.000000000000000149382522094341998803409 -0.15686274509803932453167
## 202 0.000000000000000138249984595943231525106 -0.30348258706467656775629
## 203 0.000000000000000149382522094341974151505 0.18627450980392148971454
## 204 0.000000000000000088328111868159853466717 0.00529100529100527509907
## 205 -0.000000000000000137232855194365507808939 -0.01657458563535911186260
## 207 0.000000000000000147855570572609389858893 -0.28859060402684560964204
## 208 -0.000000000000000210650008114602053769965 0.43373493975903609776168
## 209 0.000000000000000014233108656392030243716 0.29729729729729731380061
## 210 0.000000000000000243599577884415746448264 -0.53074433656957908933549
## 212 -0.000000000000000202638710266843132631132 0.44295302013422821474364
## 213 0.000000000000000043885418357208777636678 0.41666666666666679619269
## 214 -0.000000000000000140433338743068044063945 -0.00000000000000003119457
## 215 0.000000000000000322358345751133413798527 -0.18181818181818187873944
## 216 0.000000000000000254535426471810800591764 -0.07142857142857145236192
## 217 0.000000000000000089024705810337760104785 0.32142857142857139685077
## 218 -0.000000000000000140433338743068044063945 0.25000000000000000000000
## 219 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 220 -0.000000000000000450556961800676605421130 0.85416666666666662965923
## 221 0.000000000000000358357173104721231434166 -0.71270718232044205642239
## 222 -0.000000000000000001404333387430679662564 0.03999999999999998001599
## 223 0.000000000000000003900926076196336183673 -0.55555555555555558022718
## 224 -0.000000000000000106406239002333762853883 0.36231884057971031065648
## 225 0.000000000000000000000000000000000000000 0.10000000000000001942890
## 227 0.000000000000000157221720262268694845615 0.14765100671140940491455
## 228 0.000000000000000016873773259826012795181 -0.24031007751937999428193
## 229 0.000000000000000235671970404458089433352 -0.14364640883977897178880
## 230 -0.000000000000000034208120975875536078987 -0.25641025641025649850491
## 231 0.000000000000000096333845174360683296312 0.17073170731707343339423
## 232 0.000000000000000272764754097112899976555 0.11538461538461547428724
## 233 -0.000000000000000115714205137783104403325 -0.26530612244897966434110
## 234 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 235 0.000000000000000181578400659871918013267 -0.08597285067873304542641
## 236 -0.000000000000000081387503135187111196212 -0.11363636363636392401233
## 237 0.000000000000000246324606263042716611876 0.29032258064516125450893
## 238 -0.000000000000000207949366984927665936822 0.15384615384615391020517
## 239 0.000000000000000119953476843037275825582 0.16666666666666671292596
## 240 0.000000000000000105647098870931914010709 0.49541284403669721969621
## 241 -0.000000000000000140433338743068019412041 -0.12500000000000005551115
## 242 -0.000000000000000052035567480690374048922 -0.14285714285714293247942
## 244 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 245 -0.000000000000000042927221449846129724614 -0.69432314410480344690768
## 246 0.000000000000000070216669371533985054117 -0.50000000000000022204460
## 248 0.000000000000000155562897336934417729695 0.12154696132596677637938
## 249 0.000000000000000009921920672064594557989 0.60869565217391297107241
## 250 0.000000000000000153912431524210714387961 -0.10714285714285719242067
## 251 0.000000000000000174191352863997855000383 0.11538461538461534938715
## 252 -0.000000000000000155479767894111069034859 0.12244897959183710656195
## 253 -0.000000000000000203106068430057076501884 0.15702479338842975642443
## 254 0.000000000000000015264493341637834225959 0.03623188405797102967787
## 255 -0.000000000000000097190146020355003494178 0.21951219512195119243358
## 256 -0.000000000000000158234747879513258184453 -0.43661971830985918385792
## 257 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 258 0.000000000000000199678653525299891716419 0.34375000000000049960036
## 259 0.000000000000000278358939294295579857420 -0.14285714285714290472384
## 260 0.000000000000000103374541019202866111680 0.15740740740740744030290
## 261 -0.000000000000000052662502028650513442491 -0.50000000000000000000000
## 263 -0.000000000000000047499511633684765108744 0.14705882352941174184835
## 264 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 265 0.000000000000000023835016208685872252399 0.66972477064220192755073
## 266 -0.000000000000000271053788909368781100881 -0.94409937888198702804488
## 267 -0.000000000000000063762931377826850481484 -0.20588235294117643303657
## 268 -0.000000000000000105325004057301026884983 -0.37500000000000000000000
## 269 0.000000000000000140433338743068044063945 0.27586206896551723755096
## 270 0.000000000000000016987903880209844437605 0.32258064516129031362723
## 272 -0.000000000000000276357350095303569760101 -0.03669724770642209671356
## 273 0.000000000000000219427091786043808064706 0.18750000000000008326673
## 274 -0.000000000000000408786154361009881705612 -0.91089108910891081194450
## 275 -0.000000000000000118223922602071782360168 0.01657458563535911186260
## 276 0.000000000000000166037207132688186192569 -0.25414364640883974066909
## 277 -0.000000000000000123753516900021629702061 0.11877394636015328743817
## 278 0.000000000000000000000000000000000000000 1.00000000000000000000000
## 279 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 280 0.000000000000000071305299904426015302852 0.00775193798449616382207
## 281 -0.000000000000000160495244277792036271992 0.42857142857142860314923
## 282 0.000000000000000148065585413886956554692 -0.13043478260869564855362
## 283 0.000000000000000000000000000000020589057 0.00000000000000009383097
## 285 0.000000000000000122879171400184535474464 0.06508875739644973124065
## 286 0.000000000000000364777054162492035462109 -0.29875518672199163905390
## 287 -0.000000000000000097988934122961662670941 -0.29850746268656719317747
## 288 -0.000000000000000213515994619562631128802 0.18367346938775508391473
## 289 0.000000000000000083930862608161773902712 0.07812500000000008326673
## 290 0.000000000000000129083053834617250313263 0.04417670682730921727366
## 291 0.000000000000000051629903949657366240932 0.29411764705882353920785
## 294 -0.000000000000000161803629421360984246896 -0.09782608695652175723190
## 296 -0.000000000000000210650008114602053769965 -0.33333333333333337034077
## 298 0.000000000000000368637514200553631075294 1.12500000000000044408921
## 299 -0.000000000000000217913801497864194405202 0.03448275862068968244945
## 300 0.000000000000000035964635531761343539804 0.43902439024390232935602
## 303 0.000000000000000097523151904908334487966 -0.02777777777777783174695
## 305 -0.000000000000000163294579933800026011942 -0.38759689922480611290467
## 306 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 307 0.000000000000000097443541168659420659255 0.24489795918367354699008
## 308 0.000000000000000070216669371534022031972 0.28571428571428569842539
## 309 -0.000000000000000010239930950015402608109 0.54166666666666651863693
## 310 0.000000000000000381306323650842155138222 0.13207547169811320597610
## 311 0.000000000000000204956764652045235509503 0.08108108108108107170420
## 312 -0.000000000000000080964118765136168638681 -0.67346938775510201136143
## 313 -0.000000000000000136819245466592007288072 0.06862745098039213509367
## 314 -0.000000000000000184081538622670251655745 0.24324324324324320123480
## 315 0.000000000000000123911769479177664187095 0.11764705882352942400981
## 316 0.000000000000000245758342800369070948927 -0.26368159203980096050302
## 317 -0.000000000000000010971354589302189786938 0.10416666666666665741481
## 318 0.000000000000000167163548106095185821456 0.35227272727272734842430
## 319 -0.000000000000000102399309500153767236101 -0.02777777777777776235801
## 320 -0.000000000000000171490711734323467167240 -0.38461538461538458122391
## 321 0.000000000000000296649323354049779155631 -0.43119266055045890606578
## 322 0.000000000000000123353608355397589282559 0.24324324324324320123480
## 323 -0.000000000000000005401282259348769503311 0.15384615384615382693845
## 324 -0.000000000000000049035607949542302938719 -0.52066115702479365268118
## 325 -0.000000000000000198402914619567043190903 0.58139534883720933589046
## 326 -0.000000000000000231318624502190687461375 0.09677419354838716347711
## 327 -0.000000000000000274671089012177167940679 -0.20588235294117637752542
## 328 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 329 0.000000000000000189473552272393391219487 -0.34920634920634924247551
## 331 -0.000000000000000114483700062283712013119 0.36956521739130437920196
## 332 0.000000000000000125138618681941740653218 0.44554455445544532965485
## 333 -0.000000000000000005851389114294488482883 0.83333333333333370340767
## 334 -0.000000000000000232991675641908355357962 -0.09090909090909092549193
## 335 -0.000000000000000213350649244276416951205 0.15384615384615385469402
## 336 0.000000000000000226114393630951805467388 -0.09523809523809519117510
## 337 0.000000000000000013103815058772198896378 0.22535211267605631535460
## 338 0.000000000000000241163627858272022709675 -0.24832214765100657505315
## 339 -0.000000000000000110726286316649802551270 -0.38461538461538463673506
## 340 0.000000000000000140433338743068044063945 -0.66666666666666696272614
## 341 0.000000000000000103477196968576511258154 0.34210526315789441254012
## 342 0.000000000000000000000000000000000000000 -0.39737991266375544130440
## 343 0.000000000000000126292481716856313746255 -0.27777777777777773460244
## 344 0.000000000000000097322368945104125287692 0.05882352941176471200491
## 345 0.000000000000000121594720131193049014688 -0.02439024390243901135866
## 347 -0.000000000000000371644029049334724552844 -0.39779005524861876796905
## 348 0.000000000000000000000000000000000000000 0.00000000000000003204938
## 349 0.000000000000000051826589298037016848644 0.52380952380952383595769
## 352 -0.000000000000000138939367054311989765053 0.02127659574468088871102
## 354 0.000000000000000185591271089816657899392 -0.17843866171003719411914
## 355 0.000000000000000235974052805975012483204 0.09016393442622956666543
## 356 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 358 0.000000000000000155526641505173525490764 -0.59501557632398727193390
## 361 0.000000000000000076760530570360404356971 0.68639053254437865092541
## 363 -0.000000000000000267969738213813502339078 0.81632653061224513812988
## 364 0.000000000000000142182029742742257171043 0.11877394636015324580480
## 365 -0.000000000000000007744485592448608633925 -0.07352941176470592643533
## 366 0.000000000000000097523151904908371465821 -0.02777777777777774154133
## 367 -0.000000000000000069828731971691247410625 0.33149171270718214010742
## 369 0.000000000000000265414465456154472218429 -0.11003236245954688132809
## 370 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 371 -0.000000000000000018808036438803753480252 -0.26785714285714284921269
## 372 -0.000000000000000318040208329889412035921 0.76470588235294145729171
## 373 0.000000000000000157987506085951528001522 -0.50000000000000033306691
## 376 -0.000000000000000207828802648781469639735 0.08035714285714283533490
## 377 -0.000000000000000095209043215639376141833 -0.10169491525423736411060
## 378 -0.000000000000000039238727001739624474126 0.58823529411764663432649
## 379 -0.000000000000000070216669371534022031972 0.04545454545454544192928
## 380 -0.000000000000000230380311904950486196519 -0.51239669421487621381317
## 381 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 382 0.000000000000000092774965866461234027279 0.03167420814479636470562
## 383 0.000000000000000250634500395614454393256 0.22222222222222218213084
## 384 -0.000000000000000296036945560232874968762 0.08641975308641977604474
## 385 -0.000000000000000097842899943940843502218 0.40163934426229508378725
## 386 0.000000000000000123459474452841820558859 -0.10743801652892563669450
## 387 0.000000000000000070216669371534022031972 -0.00000000000000025639502
## 388 -0.000000000000000132726631129119174609098 0.14634146341463408202976
## 389 0.000000000000000226677726123321740418251 -0.22826086956521740578552
## 390 -0.000000000000000388699419735277500023825 0.63392857142857106378386
## 391 -0.000000000000000083840799249592860655973 0.19900497512437811953312
## 392 -0.000000000000000035108334685767011015986 0.00925925925925923966331
## 394 0.000000000000000166705683155571529692255 0.24832214765100665831987
## 395 -0.000000000000000093275989786446075264767 0.17159763313609457946463
## 396 -0.000000000000000356254341888907102848445 0.82945736434108530144016
## 397 -0.000000000000000193641001248205606836694 0.16149068322981369183466
## 398 0.000000000000000140433338743068044063945 0.17472118959107807434350
## 399 -0.000000000000000132331415354044880564514 0.84615384615384614530598
## 400 0.000000000000000114445674634030520456022 0.04626334519572955494837
## 401 -0.000000000000000217409998023041667610777 0.16149068322981358081236
## 402 0.000000000000000073142363928681244702999 0.20833333333333325931847
## 404 0.000000000000000252341155553950565396281 -1.25000000000000111022302
## 405 0.000000000000000103418669142236746594907 0.01357466063348420487467
## 406 0.000000000000000256443488139515528714451 0.05072463768115945820236
## 407 -0.000000000000000333979286369732308224001 -0.28205128205128204843533
## 408 -0.000000000000000457031575791049861840568 0.62721893491124258712688
## 409 -0.000000000000000105325004057301026884983 -0.61538461538461530775379
## 411 -0.000000000000000351083346857670073182007 -0.49999999999999988897770
## 412 0.000000000000000000000000000000000000000 -0.41666666666666685170384
## 413 0.000000000000000177035645117591115541798 0.51063829787234060741952
## 414 -0.000000000000000293469669424616564682559 0.58974358974358986884567
## 415 0.000000000000000170886424630722258824805 -0.22651933701657461783796
## 416 -0.000000000000000232930297434415747230423 0.21153846153846156408207
## 417 -0.000000000000000157987506085951528001522 0.33333333333333320380731
## 418 -0.000000000000000057825492423616243287311 0.05882352941176471200491
## 419 -0.000000000000000036543991494478669442049 -0.26022304832713755162743
## 420 0.000000000000000000763224667081890940926 -0.16304347826086956763092
## 421 0.000000000000000080492279523465827428515 -0.03658536585365856907970
## 423 0.000000000000000072610419463745356901472 -0.01136363636363615751967
## 425 0.000000000000000230711913649326021326109 0.14285714285714287696827
## 426 0.000000000000000205889555953820087428064 -0.64406779661016955174091
## 427 -0.000000000000000056173335497227212695197 0.05000000000000000277556
## 428 0.000000000000000172217807423081936288087 0.02366863905325448241834
## 429 0.000000000000000177240463816856047535560 0.41935483870967743547098
## 430 0.000000000000000082983336529994749948890 0.17045454545454546968486
## 431 -0.000000000000000208699545076503880670711 -0.02777777777777778664414
## 432 0.000000000000000210322911828709792124336 0.18012422360248442343433
## 433 0.000000000000000223887576930546968000957 -0.17213114754098357561674
## 435 -0.000000000000000255153530955715113723140 -0.59154929577464798740039
## 436 -0.000000000000000055460992474617444324647 -0.07246376811594200384459
## 437 0.000000000000000178983667025478872922152 -0.27450980392156870690812
## 438 -0.000000000000000039009260761963357214494 -0.27777777777777790113589
## 440 0.000000000000000280866677486136088127889 -0.18473895582329324938087
## 441 0.000000000000000045994640014687011517520 -0.03875968992248064043382
## 442 0.000000000000000402947932188916791900063 0.57954545454545436378169
## 443 0.000000000000000200800611053436636419453 0.22171945701357470559500
## 445 -0.000000000000000280866677486136088127889 0.28571428571428575393654
## 446 -0.000000000000000000000000000000000906222 0.00000000000000001290608
## 447 0.000000000000000280866677486136088127889 -0.53846153846153843591793
## 448 -0.000000000000000131452136846709037949426 0.12403100775193798277485
## 449 0.000000000000000013798035466021967847457 -0.19650655021834059765773
## 450 -0.000000000000000255669308094437258052440 -0.32057416267942590026863
## 451 0.000000000000000055983560715141976380817 0.70270270270270285273284
## 452 0.000000000000000302321770905215893612072 -0.43518518518518506388304
## 453 0.000000000000000074347061687506623164160 0.23529411764705879250847
## 454 -0.000000000000000119654936582103889027261 0.59183673469387743093506
## 455 0.000000000000000138252696837119748135298 -0.32298136645962727264703
## 456 0.000000000000000000000000000000000000000 0.00000000000000000000000
## 457 0.000000000000000090360795830580660119453 0.16393442622950823994721
## 459 -0.000000000000000154476672617374841074768 -0.59999999999999997779554
## 460 0.000000000000000067951615520839340772564 0.75806451612903236192409
## 461 -0.000000000000000082607846319451739146875 0.29411764705882337267440
## 462 -0.000000000000000101629389879851835393953 -0.52631578947368407028762
## 463 -0.000000000000000021809723062370416867957 -0.18939393939393936450166
## 464 -0.000000000000000069835057037993078872625 -0.40217391304347821501253
## 474 -0.000000000000000175541673428835036591003 0.21428571428571441259692
## 476 0.000000000000000010127404236278940507592 0.09615384615384611755040
## 481 -0.000000000000000140433338743068044063945 0.25000000000000005551115
## 482 -0.000000000000000011606061053146119229527 -0.08264462809917355601286
## 483 -0.000000000000000007564779296933222269986 -0.30386740331491712829859
## 489 0.000000000000000447758471354709631398144 0.20289855072463783280945
## 498 0.000000000000000000948873910426138045883 0.02702702702702710138571
## 501 -0.000000000000000090686811356092517879575 -0.07641196013289038024840
## 508 0.000000000000000026545326225823836770712 -0.06097560975609756656057
## 510 0.000000000000000163548142946119606198672 0.06832298136645958974711
## 512 -0.000000000000000113292853063999917407328 0.02836879432624114100170
## 515 0.000000000000000200619055347240045339990 0.14285714285714287696827
## 517 0.000000000000000030528986683275656125967 0.10869565217391304046135
## 520 -0.000000000000000308405784051577171570610 0.33027522935779823898272
## 523 -0.000000000000000018004274197829233732029 0.12820512820512819374130
## 527 0.000000000000000247358584574720302098575 -0.13259668508287292265635
## 530 -0.000000000000000147542051138120026703850 0.14049586776859507297743
## 536 -0.000000000000000050712038990552334180261 -0.51388888888888895056795
## 540 0.000000000000000211682606193595231786403 -0.25490196078431381909724
##
## Degrees of freedom: 4240 total; 3392 residual
## Residual standard error: 1.05803
Uncentered variables
eli_coeffs_per_sub <- lmList(eli_targ ~ 1 + eli_self | sub_id, eli_data)
eli_coeffs_per_sub
## Call:
## Model: eli_targ ~ 1 + eli_self | sub_id
## Data: eli_data
##
## Coefficients:
## (Intercept) eli_self
## 1 4.0677966101694913447773 -0.271186440677966267287502
## 2 2.3281250000000000000000 0.132812499999999916733273
## 3 3.3214285714285707307170 -0.035714285714285698425385
## 4 2.7434944237918204024140 0.308550185873606108710732
## 5 2.7272727272727270708685 0.204545454545454530315141
## 6 2.2096069868995620311125 0.222707423580785879302368
## 8 4.8196721311475414495362 -0.491803278688524636574897
## 10 3.5692307692307680966337 -0.076923076923076788569134
## 11 1.0000000000000013322676 0.749999999999999777955395
## 12 2.7999999999999984900967 0.000000000000000219427092
## 13 3.0000000000000004440892 0.125000000000000083266727
## 14 3.5100671140939598835473 -0.026845637583892714012057
## 15 4.2483221476510060199416 -0.328859060402684477697477
## 16 2.0567375886524805750355 0.390070921985815610710802
## 17 2.9999999999999991118216 0.000000000000000052012348
## 19 4.1874999999999991118216 -0.562500000000000222044605
## 20 2.6714285714285712636240 0.142857142857142876968268
## 21 2.9999999999999960031971 0.000000000000000672553716
## 22 2.9999999999999991118216 -0.000000000000000024212645
## 23 2.9999999999999991118216 0.000000000000000000000000
## 25 4.1923076923076916244781 -0.192307692307692262856378
## 26 3.5140845070422530582732 -0.239436619718309706694725
## 27 3.6763485477178421412248 -0.265560165975103679159020
## 29 3.2173913043478266082786 0.072463768115941920577860
## 30 0.6938775510204090446464 0.693877551020408045445720
## 32 2.9999999999999991118216 -0.000000000000000084825507
## 33 3.8837209302325574888926 -0.124031007751938024408211
## 34 3.8993288590604020527053 -0.281879194630872520477283
## 35 3.4617940199335550666149 -0.109634551495016580036079
## 36 3.6666666666666674068153 -0.190476190476190937461709
## 37 3.1020408163265313916668 0.030612244897959023370859
## 38 2.8859060402684550972197 0.073825503355704688579486
## 39 2.9285714285714274929262 0.132653061224489832170548
## 40 2.2758620689655177926625 0.206896551724137872652065
## 41 2.1562500000000000000000 0.218749999999999861222122
## 42 2.7377049180327852617722 0.049180327868852811989964
## 43 2.9090909090909091716526 0.053030303030303080347174
## 44 4.1203703703703693506100 -0.268518518518518378712656
## 45 5.7352941176470570994184 -0.705882352941176405280999
## 47 2.6716417910447765038384 0.084577114427860630718747
## 48 6.3827160493827150844481 -1.049382716049382935352696
## 50 2.5394736842105261054314 0.223684210526315735423353
## 51 3.4343891402714921134987 -0.040723981900452434212756
## 52 3.6666666666666674068153 -0.083333333333333453607494
## 53 3.4074074074074069962137 -0.064814814814814949994748
## 54 4.8852459016393439128478 -0.508196721311475418936254
## 55 3.8374999999999972466469 -0.312499999999999777955395
## 58 2.4888888888888902606311 0.088888888888888711869996
## 59 2.9153439153439153486147 -0.005291005291005274231708
## 60 1.8529411764705878695736 0.401960784313725505434434
## 61 3.7209302325581399273347 -0.279069767441860572265711
## 62 4.9185520361990944238073 -0.429864253393665074476360
## 63 4.0000000000000008881784 0.000000000000000000000000
## 64 2.7499999999999982236432 0.035714285714286010675611
## 65 4.3076923076923065991650 -0.207100591715976528606191
## 66 4.4899598393574295940311 -0.341365461847389528582397
## 67 3.2281879194630858087578 -0.161073825503355472221756
## 68 2.1311475409836058148016 0.352459016393442792214330
## 69 3.0000000000000008881784 -0.000000000000000208766557
## 71 2.4033149171270715704907 0.220994475138121537760583
## 72 3.5961538461538458122391 -0.057692307692307667754683
## 73 2.7846889952153097702592 0.133971291866028574535363
## 74 6.0434782608695645222951 -0.956521739130434700548733
## 75 3.7796610169491526853847 -0.118644067796610297449433
## 76 5.0317460317460307450688 -0.349206349206349242475511
## 78 0.2690763052208833272516 0.714859437751004023198220
## 80 3.6000000000000005329071 -0.333333333333333481363070
## 81 2.7999999999999993782751 -0.000000000000000051624322
## 82 2.2500000000000000000000 0.250000000000000000000000
## 83 2.9999999999999991118216 -0.000000000000000066873018
## 84 2.9999999999999986677324 0.000000000000000110143795
## 85 4.9830508474576271638057 -0.432203389830508433178125
## 86 4.6101694915254229911739 -0.440677966101694851275283
## 87 2.9999999999999986677324 0.000000000000000127666672
## 88 2.9999999999999991118216 -0.000000000000000020352658
## 89 2.9897959183673465943798 -0.153061224489795977277140
## 90 3.2837837837837828836030 -0.101351351351351537388723
## 91 3.0000000000000000000000 -0.000000000000000143663804
## 92 2.5000000000000000000000 0.272727272727272873620308
## 93 3.0000000000000000000000 -0.000000000000000100309528
## 94 2.9999999999999986677324 0.000000000000000117027782
## 95 2.2173913043478261641894 0.405797101449275277040840
## 96 3.0212765957446809927944 0.021276595744680888711020
## 97 3.6749999999999998223643 -0.125000000000000111022302
## 98 4.1739130434782589773590 -0.326086956521739024239537
## 99 4.1666666666666669627261 -0.166666666666666685170384
## 100 2.9999999999999986677324 0.000000000000000084825507
## 101 3.4202898550724634141318 0.057971014492753734914654
## 102 3.8888888888888883954564 -0.055555555555555712066162
## 103 3.0500000000000002664535 0.083333333333333217685102
## 104 2.6990291262135919225784 0.097087378640776683669245
## 105 2.1379310344827593404204 0.206896551724137817140914
## 106 2.9378881987577636358822 -0.037267080745341608916110
## 107 4.1340996168582373471168 -0.306513409961685856508495
## 108 2.7181818181818178103981 0.227272727272727320668722
## 110 3.4999999999999991118216 -0.117647058823529437887601
## 111 3.3793103448275858546879 0.034482758620689675510551
## 112 2.9999999999999991118216 -0.000000000000000012883793
## 113 2.6016949152542361289875 0.093220338983050959891230
## 114 3.0294117647058826925388 0.019607843137254912097012
## 115 1.7222222222222214327303 0.388888888888888950567946
## 116 1.8217054263565888305720 0.573643410852713309111550
## 117 6.6363636363636375747888 -1.454545454545455029915502
## 118 3.1375661375661376695234 -0.116402116402116562188240
## 120 3.0000000000000000000000 0.000000000000000000000000
## 121 3.0538461538461536548539 -0.038461538461538540001339
## 122 2.5436241610738257179491 0.295302013422818698806793
## 123 4.0000000000000000000000 -0.222222222222222265397562
## 124 3.9130434782608696231421 -0.521739130434782594214482
## 125 3.0655737704918024633116 0.098360655737704930090537
## 126 2.8000000000000002664535 0.299999999999999766853165
## 127 1.6304347826086957873315 0.380434782608695398753440
## 129 1.5186721991701241929462 0.427385892116182608546637
## 130 3.5968992248062017225152 -0.240310077519380160815388
## 131 2.9111111111111105387295 0.311111111111111005023133
## 132 2.9999999999999991118216 0.000000000000000066873018
## 133 2.9999999999999982236432 0.000000000000000223879236
## 134 6.4426229508196719564239 -0.754098360655738098046186
## 135 0.3812154696132613040938 0.734806629834253932642696
## 136 1.6829268292682926233539 0.475609756097560898435717
## 137 2.5291005291005292932027 0.248677248677248580177590
## 138 2.6999999999999997335465 0.000000000000000000000000
## 139 2.5502645502645511221829 0.280423280423280407713804
## 140 3.4615384615384621191936 -0.118343195266272169230426
## 141 4.0852713178294575158134 -0.317829457364340983716033
## 142 2.8783068783068785911894 0.179894179894179884193761
## 143 4.3333333333333339254523 -0.333333333333333314829616
## 144 3.2986425339366514108974 -0.221719457013574511305976
## 145 3.0200803212851417001161 0.068273092369477900165364
## 146 4.0091743119266052275407 -0.284403669724770713500561
## 147 2.9999999999999991118216 0.000000000000000067951616
## 148 3.0400000000000000355271 -0.040000000000000042466031
## 149 3.9724770642201825410211 -0.128440366972477126861207
## 150 2.8731707317073182927913 0.121951219512195035976632
## 151 3.5076923076923076649791 -0.230769230769230754285459
## 153 1.4719101123595497160323 0.348314606741573218329222
## 154 5.2499999999999982236432 -0.652777777777777568068984
## 155 4.1315789473684194632597 -0.342105263157894745607024
## 156 3.1086956521739126380055 -0.065217391304347838154598
## 157 4.5072463768115929028113 -0.318840579710144678138306
## 158 5.6129032258064510685358 -0.725806451612903025250034
## 159 0.5925925925925926707194 0.790123456790123190884856
## 160 3.0163934426229506158279 -0.065573770491803226589944
## 161 4.8459016393442615466824 -0.213114754098360809075885
## 162 4.4857142857142857650388 -0.428571428571428658660381
## 163 1.2999999999999984900967 0.500000000000000222044605
## 164 2.5499999999999998223643 0.249999999999999972244424
## 165 3.0679012345679010920207 -0.049382716049382761880349
## 166 1.1900826446280976522729 0.570247933884297619755444
## 168 2.6666666666666674068153 0.111111111111111007798691
## 169 4.1048034934497801273778 -0.257641921397379791702775
## 171 4.1285714285714272264727 -0.357142857142857150787307
## 172 2.8735632183908048631338 0.038314176245210669613517
## 173 3.3850931677018634147203 0.031055900621117946136529
## 174 2.7857142857142860314923 0.112244897959183659308380
## 175 2.8499999999999996447286 0.249999999999999972244424
## 176 3.6803278688524585504638 -0.229508196721311646948394
## 177 2.6111111111111116045436 0.055555555555555607982754
## 178 2.8732394366197189228274 0.154929577464788748031310
## 179 2.7499999999999995559108 0.107142857142857247931822
## 180 1.8513011152416360971529 0.338289962825278789360084
## 181 2.7826086956521733917214 0.130434782608695676309196
## 182 1.5999999999999994226840 0.400000000000000022204460
## 184 2.7706422018348617619665 0.110091743119266088912767
## 185 2.8439716312056733116265 0.177304964539007181567598
## 186 3.8868778280542963798894 -0.208144796380090368881355
## 187 2.7142857142857144125969 0.107142857142857123031732
## 189 3.8125000000000004440892 -0.218750000000000027755576
## 190 3.1999999999999997335465 0.000000000000000054663603
## 191 3.9629629629629641307531 -0.148148148148148445235606
## 192 3.9069767441860481227422 -0.232558139534884023014172
## 193 -0.1322751322751309910331 0.788359788359788038469844
## 194 1.1666666666666667406815 0.539215686274509553577161
## 195 2.6434108527131781052333 0.147286821705426396178495
## 196 2.4378378378378369184531 0.189189189189189338469177
## 198 4.2303030303030304537515 -0.151515151515151547112481
## 199 0.3707865168539324352004 0.674157303370786609164611
## 200 2.6078431372549006894701 -0.156862745098039102487064
## 202 4.1194029850746263221595 -0.303482587064676623267445
## 203 2.6666666666666665186369 0.186274509803921461958964
## 204 2.8835978835978832712783 0.005291005291005297650475
## 205 3.1546961325966846878543 -0.016574585635359215946005
## 207 4.1946308724832208625344 -0.288590604026845609642038
## 208 1.8554216867469870422269 0.433734939759036208783982
## 209 2.4594594594594596514980 0.297297297297297258289461
## 210 5.0453074433656928121650 -0.530744336569579200357794
## 212 0.8724832214765095850240 0.442953020134228325765946
## 213 1.6666666666666662965923 0.416666666666666796192686
## 214 3.5999999999999992006394 0.000000000000000093583722
## 215 3.9999999999999995559108 -0.181818181818181795472711
## 216 4.0142857142857142349612 -0.071428571428571424606346
## 217 2.1785714285714261606586 0.321428571428571674406527
## 218 2.1000000000000000888178 0.249999999999999972244424
## 219 2.9999999999999991118216 -0.000000000000000091406315
## 220 -0.1458333333333320103176 0.854166666666666629659233
## 221 5.6519337016574571208594 -0.712707182320441945400091
## 222 3.1599999999999996980193 0.040000000000000111854970
## 223 5.0444444444444442865461 -0.555555555555555580227178
## 224 2.0144927536231880260686 0.362318840579710255145329
## 225 2.5999999999999987565502 0.100000000000000158206781
## 227 3.1946308724832213066236 0.147651006711409460425699
## 228 3.8372093023255815502637 -0.240310077519379994281934
## 229 3.3314917127071823621520 -0.143646408839779110566681
## 230 4.4743589743589735618912 -0.256410256410256720549512
## 231 3.2195121951219514144782 0.170731707317073072571745
## 232 2.9615384615384612310152 0.115384615384615460409456
## 233 3.9489795918367338600774 -0.265306122448979664341095
## 234 2.9999999999999991118216 0.000000000000000014329933
## 235 3.5837104072398182630366 -0.085972850678733059304193
## 236 2.3181818181818178992160 -0.113636363636363604823210
## 237 2.3548387096774194837678 0.290322580645161254508935
## 238 3.2692307692307696065370 0.153846153846153771427296
## 239 3.1666666666666665186369 0.166666666666666685170384
## 240 1.4678899082568810374738 0.495412844036697219696208
## 241 3.1249999999999995559108 -0.124999999999999916733273
## 242 3.6428571428571423496123 -0.142857142857142793701541
## 244 2.9999999999999991118216 0.000000000000000009425056
## 245 4.8078602620087353969325 -0.694323144104803779974588
## 246 4.4999999999999991118216 -0.499999999999999944488849
## 248 2.9502762430939228899263 0.121546961325966859646108
## 249 1.3913043478260882501729 0.608695652173912860050109
## 250 3.7499999999999991118216 -0.107142857142857053642793
## 251 2.8846153846153836930455 0.115384615384615432653881
## 252 2.1224489795918377588180 0.122448979591836495939283
## 253 3.1818181818181821007840 0.157024793388429728668854
## 254 3.3840579710144922387371 0.036231884057970974166718
## 255 2.3536585365853666118596 0.219512195121951136922434
## 256 4.0845070422535210141746 -0.436619718309859239369075
## 257 2.9999999999999991118216 -0.000000000000000050154764
## 258 2.0624999999999986677324 0.343750000000000000000000
## 259 3.2857142857142851433139 -0.142857142857142821457117
## 260 2.7962962962962953916701 0.157407407407407495814056
## 261 4.9999999999999991118216 -0.500000000000000000000000
## 263 2.0882352941176471894380 0.147058823529411630826047
## 264 2.9999999999999991118216 -0.000000000000000041303923
## 265 1.6238532110091756788250 0.669724770642201594483822
## 266 6.0931677018633507714185 -0.944099378881987583156388
## 267 3.3823529411764705621124 -0.205882352941176599570028
## 268 5.3249999999999992894573 -0.375000000000000222044605
## 269 2.4344827586206898573096 0.275862068965516959995199
## 270 1.3387096774193543158304 0.322580645161290535671839
## 272 3.7431192660550469675229 -0.036697247706422360391532
## 273 2.2374999999999998223643 0.187500000000000055511151
## 274 5.9702970297029711588266 -0.910891089108911033989102
## 275 3.0386740331491717270751 0.016574585635359115332044
## 276 3.7403314917127064020974 -0.254143646408839907202548
## 277 2.2605363984674333721614 0.118773946360153162538076
## 278 0.0000000000000005617334 0.999999999999999777955395
## 279 3.0000000000000000000000 -0.000000000000000175541673
## 280 2.8759689922480613510913 0.007751937984496039789339
## 281 1.6428571428571430157461 0.428571428571428714171532
## 282 3.7826086956521733917214 -0.130434782608695648553621
## 283 3.0000000000000000000000 0.000000000000000000000000
## 285 2.6982248520710063743877 0.065088757396449717362863
## 286 4.3858921161825721313221 -0.298755186721991639053897
## 287 4.8059701492537305611563 -0.298507462686567304199770
## 288 2.6122448979591830209301 0.183673469387755056159151
## 289 3.7500000000000000000000 0.078124999999999791833183
## 290 2.7630522088353410836703 0.044176706827309251968128
## 291 2.0588235294117649409884 0.294117647058823539207850
## 294 4.0326086956521738358106 -0.097826086956521743354109
## 296 4.3333333333333321490954 -0.333333333333333259318465
## 298 -0.6250000000000016653345 1.125000000000000666133815
## 299 3.1034482758620676179362 0.034482758620689391015901
## 300 1.5121951219512204112050 0.439024390243902329356018
## 303 3.5000000000000000000000 -0.027777777777777741541332
## 305 4.5891472868217064728924 -0.387596899224806390460429
## 306 2.9999999999999991118216 -0.000000000000000058513891
## 307 2.7959183673469385489341 0.244897959183673352701049
## 308 2.3999999999999999111822 0.285714285714285809447688
## 309 1.2500000000000002220446 0.541666666666666518636930
## 310 3.6377358490566029658453 0.132075471698113233731675
## 311 3.0162162162162164946722 0.081081081081081182726500
## 312 5.3265306122448956571702 -0.673469387755101345227615
## 313 2.8529411764705887577520 0.068627450980392010193576
## 314 2.2486486486486487734737 0.243243243243243090212502
## 315 3.0588235294117649409884 0.117647058823529326865298
## 316 3.6119402985074624545803 -0.263681592039801127036469
## 317 2.6041666666666660745477 0.104166666666666657414808
## 318 1.7727272727272724850422 0.352272727272727292913146
## 319 3.1944444444444446418174 -0.027777777777777738071885
## 320 4.0461538461538451016963 -0.384615384615384636735058
## 321 4.9816513761467886567402 -0.431192660550458684021180
## 322 2.6918918918918919302996 0.243243243243243395523834
## 323 2.4615384615384612310152 0.153846153846153854694023
## 324 4.8181818181818183433052 -0.520661157024793652681183
## 325 1.2325581395348843560811 0.581395348837208780778951
## 326 2.8709677419354826533038 0.096774193548387177354897
## 327 3.9411764705882337267440 -0.205882352941176266503120
## 328 3.0000000000000000000000 -0.000000000000000072017097
## 329 4.4126984126984121203918 -0.349206349206349131453209
## 331 2.3695652173913046567577 0.369565217391304434713106
## 332 0.8514851485148534626646 0.445544554455445163121396
## 333 0.2666666666666663298990 0.833333333333333370340767
## 334 3.5454545454545458582629 -0.090909090909090856102992
## 335 2.7692307692307687183586 0.153846153846153854694023
## 336 3.1333333333333337478166 -0.095238095238095454853067
## 337 2.6338028169014084944877 0.225352112676056398621327
## 338 3.2684563758389240106794 -0.248322147651006297497389
## 339 3.8846153846153854694023 -0.384615384615384803268512
## 340 5.5666666666666664298191 -0.666666666666666629659233
## 341 2.0000000000000008881784 0.342105263157894745607024
## 342 4.0524017467248896195997 -0.397379912663755274770949
## 343 4.5555555555555553581826 -0.277777777777777679091287
## 344 2.7352941176470584316860 0.058823529411764587104816
## 345 2.9878048780487800328842 -0.024390243902439125850412
## 347 4.0718232044198874319818 -0.397790055248618434902141
## 348 3.0000000000000004440892 -0.000000000000000352543159
## 349 1.6666666666666654084139 0.523809523809524169024598
## 352 3.0212765957446805487052 0.021276595744681128102860
## 354 3.3531598513011156015295 -0.178438661710037194119138
## 355 3.0655737704918029074008 0.090163934426229511154283
## 356 2.9999999999999986677324 0.000000000000000100309528
## 358 4.9065420560747652345412 -0.595015576323987605000809
## 361 1.2721893491124263153580 0.686390532544378650925410
## 363 0.3428571428571421941811 0.816326530612245138129879
## 364 2.9080459770114943651720 0.118773946360153176415864
## 365 3.7352941176470579875968 -0.073529411764705857046387
## 366 2.5000000000000013322676 -0.027777777777778032974876
## 367 2.2375690607734806114593 0.331491712707182140107420
## 369 3.7411003236245958802897 -0.110032362459547075617117
## 370 2.9999999999999991118216 -0.000000000000000082607846
## 371 3.9642857142857135244185 -0.267857142857142904723844
## 372 1.0588235294117651630330 0.764705882352941124224799
## 373 5.2999999999999989341859 -0.500000000000000000000000
## 376 2.9107142857142846992247 0.080357142857142654923663
## 377 3.0254237288135583661131 -0.101694915254237253088299
## 378 0.3823529411764708396682 0.588235294117647078415700
## 379 3.3636363636363637574789 0.045454545454545525196011
## 380 5.4958677685950405589210 -0.512396694214876102790868
## 381 3.0000000000000000000000 0.000000000000000000000000
## 382 2.7828054298642532771169 0.031674208144796371644514
## 383 2.1111111111111116045436 0.222222222222222154375260
## 384 2.8148148148148153246950 0.086419753086419540122343
## 385 2.1557377049180326267219 0.401639344262295305831856
## 386 3.1900826446280992065851 -0.107438016528925581183351
## 387 2.4000000000000012434498 -0.000000000000000256395025
## 388 2.5731707317073166940702 0.146341463414634165296491
## 389 4.3478260869565206192533 -0.228260869565217405785518
## 390 1.0446428571428563181200 0.633928571428571618895376
## 391 2.3432835820895521194984 0.199004975124378119533120
## 392 3.0648148148148153246950 0.009259259259259095681260
## 394 2.5302013422818787624635 0.248322147651006658319872
## 395 2.5680473372781067453730 0.171597633136094662731352
## 396 0.4651162790697663806938 0.829457364341085412462462
## 397 2.7639751552795019939879 0.161490683229813553056786
## 398 2.7583643122676577696950 0.174721189591078074343500
## 399 -0.0769230769230762889688 0.846153846153846145305977
## 400 2.7473309608540921189501 0.046263345195729617398417
## 401 2.7639751552795028821663 0.161490683229813636323513
## 402 2.0833333333333339254523 0.208333333333333092785011
## 404 9.0000000000000124344979 -1.250000000000002664535259
## 405 2.8552036199095018886851 0.013574660633484208344113
## 406 3.6376811594202886901428 0.050724637681159430446787
## 407 4.8846153846153850253131 -0.282051282051282048435326
## 408 1.1538461538461548538947 0.627218934911242365082273
## 409 5.3538461538461534772182 -0.615384615384615307753791
## 411 5.1999999999999975131004 -0.499999999999999611421941
## 412 4.2500000000000008881784 -0.416666666666666629659233
## 413 1.5106382978723404963972 0.510638297872340385374912
## 414 0.8589743589743582541374 0.589743589743589979867977
## 415 4.1381215469613259472226 -0.226519337016574701104688
## 416 2.4807692307692308375522 0.211538461538461536326494
## 417 2.0000000000000004440892 0.333333333333333314829616
## 418 2.8529411764705887577520 0.058823529411764649554861
## 419 3.8066914498141266598452 -0.260223048327137662649733
## 420 4.0869565217391299327687 -0.163043478260869567630920
## 421 3.0243902439024372696963 -0.036585365853658353973987
## 423 2.4431818181818170110375 -0.011363636363635949352857
## 425 2.8571428571428558740308 0.142857142857142932479420
## 426 4.8474576271186426978943 -0.644067796610169440718607
## 427 3.3999999999999994670929 0.050000000000000002775558
## 428 3.3076923076923065991650 0.023668639053254360987699
## 429 1.7903225806451612545089 0.419354838709677435470979
## 430 2.9545454545454541417371 0.170454545454545386418133
## 431 3.3055555555555549140934 -0.027777777777777849094187
## 432 2.6335403726708066507456 0.180124223602484395678758
## 433 3.8852459016393434687586 -0.172131147540983575616735
## 435 5.2112676056338029795256 -0.591549295774647876378083
## 436 2.7318840579710146343473 -0.072463768115942031600163
## 437 3.8333333333333330372739 -0.274509803921568540374665
## 438 4.4999999999999982236432 -0.277777777777777568068984
## 440 3.9357429718875502899778 -0.184738955823293277136443
## 441 3.6511627906976737989453 -0.038759689922480515533731
## 442 1.5454545454545458582629 0.579545454545454363781687
## 443 2.4796380090497733839072 0.221719457013574761106156
## 445 2.4571428571428568510271 0.285714285714285420869629
## 446 3.9999999999999995559108 0.000000000000000012906081
## 447 4.7846153846153844924061 -0.538461538461538435917930
## 448 3.1162790697674416229290 0.124031007751937982774848
## 449 3.0698689956331874917339 -0.196506550218340486635427
## 450 3.6937799043062198300902 -0.320574162679425900268626
## 451 0.9405405405405404817287 0.702702702702702963755144
## 452 4.0185185185185181566681 -0.435185185185185119394191
## 453 2.4999999999999995559108 0.235294117647058736997323
## 454 1.6285714285714276705619 0.591836734693877764001968
## 455 4.4658385093167689561255 -0.322981366459627328158177
## 456 2.9999999999999991118216 -0.000000000000000060473687
## 457 2.0737704918032782153148 0.163934426229508156680481
## 459 4.4999999999999991118216 -0.599999999999999977795540
## 460 0.8225806451612905911830 0.758064516129032139879484
## 461 1.9999999999999991118216 0.294117647058823594719001
## 462 4.6842105263157893801917 -0.526315789473684625399130
## 463 2.9545454545454550299155 -0.189393939393939392257238
## 464 4.4673913043478261641894 -0.402173913043478492568283
## 474 3.0571428571428564957557 0.214285714285714412596917
## 476 2.6538461538461537436717 0.096153846153846117550401
## 481 2.1000000000000000888178 0.249999999999999916733273
## 482 3.3057851239669422405143 -0.082644628099173611524009
## 483 4.6243093922651929972290 -0.303867403314917128298589
## 489 2.2318840579710137461689 0.202898550724637277697937
## 498 3.4054054054054048172873 0.027027027027027018118988
## 501 3.3521594684385385143344 -0.076411960132890408003981
## 508 3.7195121951219514144782 -0.060975609756097469416058
## 510 3.0745341614906820382203 0.068322981366459645258260
## 512 3.1063829787234040757937 0.028368794326241151410040
## 515 2.7999999999999993782751 0.142857142857142627168088
## 517 2.7173913043478261641894 0.108695652173912846172321
## 520 2.3119266055045870622564 0.330275229357798238982724
## 523 2.6410256410256409687065 0.128205128205128249252454
## 527 4.2375690607734801673701 -0.132596685082872922656350
## 530 2.6363636363636366866103 0.140495867768595072977433
## 536 5.3472222222222214327303 -0.513888888888888950567946
## 540 4.1666666666666660745477 -0.254901960784313763586084
##
## Degrees of freedom: 4240 total; 3392 residual
## Residual standard error: 1.05803
There seems to be variability in the intercepts, even though lmer is not finding it.
ggplot(clean_data_eli, aes(eli_self_pmc, eli_targ_pmc)) +
geom_point()
eli_randslopes_test <- lmer(eli_targ_pmc ~ eli_self_pmc + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc | sub_id),
data = eli_data)
summary(eli_randslopes_test)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc + (0 + eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12253.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0943 -0.5982 0.0068 0.6452 3.2360
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.04537 0.2130
## Residual 0.99461 0.9973
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.000000000000000005827 0.015315897842633369522 0.000
## eli_self_pmc 0.012234199608380517260 0.016068790172722027115 0.761
##
## Correlation of Fixed Effects:
## (Intr)
## eli_slf_pmc 0.000
Running without the random intercept fixes the issue
comp_eli_randslopes <- lmer(eli_targ_pmc ~ eli_self_pmc*itt_comp_gmc + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(comp_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc * itt_comp_gmc + (0 + eli_self_pmc |
## sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12233
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2229 -0.6049 0.0141 0.6519 3.1456
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.03695 0.1922
## Residual 0.99503 0.9975
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 0.00000000000000000569 0.01531913279267939354
## eli_self_pmc 0.01357934379323691258 0.01540005841078596720
## itt_comp_gmc 0.00000000000000001164 0.01430741929851506340
## eli_self_pmc:itt_comp_gmc -0.08568263438990457448 0.01433422509918278950
## t value
## (Intercept) 0.000
## eli_self_pmc 0.882
## itt_comp_gmc 0.000
## eli_self_pmc:itt_comp_gmc -5.977
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ itt_c_
## eli_slf_pmc 0.000
## itt_cmp_gmc 0.000 0.000
## el_slf_p:__ 0.000 -0.017 0.000
tab_model(comp_eli_randslopes,
digits = 3)
| eli_targ_pmc | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.000 | -0.030 – 0.030 | 1.000 |
| eli self pmc | 0.014 | -0.017 – 0.044 | 0.378 |
| itt comp gmc | 0.000 | -0.028 – 0.028 | 1.000 |
|
eli self pmc * itt comp gmc |
-0.086 | -0.114 – -0.058 | <0.001 |
| Random Effects | |||
| σ2 | 1.00 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.04 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.06 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.013 / 0.071 | ||
threat_levels = list(itt_comp_gmc = c(-1.07, 0.0, 1.07))
comp_simpslopes_eli <- emtrends(comp_eli_randslopes, ~ itt_comp_gmc,
var ="eli_self_pmc",
at = threat_levels)
comp_simpslopes_eli
## itt_comp_gmc eli_self_pmc.trend SE df asymp.LCL asymp.UCL
## -1.07 0.1053 0.0219 Inf 0.0623 0.1482
## 0.00 0.0136 0.0154 Inf -0.0166 0.0438
## 1.07 -0.0781 0.0215 Inf -0.1203 -0.0359
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
comp_eli_maineffect <- effect("eli_self_pmc:itt_comp_gmc",
xlevels = list(itt_comp_gmc = c(-1.07, 0, 1.07)),
mod = comp_eli_randslopes)
comp_eli_maineffect <- as.data.frame(comp_eli_maineffect)
comp_eli_maineffect$itt_comp_gmc <- as.factor(comp_eli_maineffect$itt_comp_gmc)
ggplot(comp_eli_maineffect, aes(eli_self_pmc, fit, group = itt_comp_gmc)) +
geom_smooth(method = "lm",
size = .7,
se = FALSE,
colour = "black",
aes(linetype = itt_comp_gmc)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
scale_linetype_manual("Target-level threat",
breaks = c(-1.07, 0, 1.07),
labels = c("Low",
"Average",
"High"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Projection by target-level threat",
subtitle = "Using the ELI",
x = "ELI responses for self",
y = "ELI responses for target")
# checking normality of conditional residuals
qqnorm(residuals(comp_eli_randslopes), main="Q-Q plot for conditional residuals")
# checking the normality of the random effects (here random intercept):
qqnorm(ranef(comp_eli_randslopes)$sub_id$eli_self_pmc,
main="Q-Q plot for the self random effect")
plot_model(comp_eli_randslopes, type='diag')
## [[1]]
##
## [[2]]
## [[2]]$sub_id
##
##
## [[3]]
##
## [[4]]
Also seems evenly spread but diagonal
compcond_eli_randslopes <- lmer(eli_targ_pmc ~ eli_self_pmc*itt_comp_gmc*target_condition + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(compcond_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc * itt_comp_gmc * target_condition +
## (0 + eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12252.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2379 -0.6090 0.0184 0.6525 3.1815
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.03419 0.1849
## Residual 0.99598 0.9980
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate
## (Intercept) 0.000000000000000004358
## eli_self_pmc 0.088481139932443900187
## itt_comp_gmc 0.000000000000000008997
## target_conditionLOSS -0.000000000000000007145
## target_conditionWARM 0.000000000000000002538
## eli_self_pmc:itt_comp_gmc -0.049852936733676501968
## eli_self_pmc:target_conditionLOSS -0.118708640672557777518
## eli_self_pmc:target_conditionWARM -0.104016945472126712269
## itt_comp_gmc:target_conditionLOSS 0.000000000000000002307
## itt_comp_gmc:target_conditionWARM 0.000000000000000012476
## eli_self_pmc:itt_comp_gmc:target_conditionLOSS -0.053543741486550715247
## eli_self_pmc:itt_comp_gmc:target_conditionWARM 0.054026394796160101541
## Std. Error t value
## (Intercept) 0.041636752215280230238 0.000
## eli_self_pmc 0.040505094593943948011 2.184
## itt_comp_gmc 0.032985381665870824874 0.000
## target_conditionLOSS 0.057353512482594334876 0.000
## target_conditionWARM 0.051023008885615041275 0.000
## eli_self_pmc:itt_comp_gmc 0.032253424624806362186 -1.546
## eli_self_pmc:target_conditionLOSS 0.056651859972297845258 -2.095
## eli_self_pmc:target_conditionWARM 0.050034260083405897312 -2.079
## itt_comp_gmc:target_conditionLOSS 0.051791969957401644276 0.000
## itt_comp_gmc:target_conditionWARM 0.046193712076551797507 0.000
## eli_self_pmc:itt_comp_gmc:target_conditionLOSS 0.050683447925176337845 -1.056
## eli_self_pmc:itt_comp_gmc:target_conditionWARM 0.045640643166038177836 1.184
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ itt_c_ t_LOSS t_WARM el__:__ e__:_L e__:_W i__:_L
## eli_slf_pmc 0.000
## itt_cmp_gmc 0.787 0.000
## trgt_cnLOSS -0.726 0.000 -0.571
## trgt_cnWARM -0.816 0.000 -0.642 0.592
## el_slf_p:__ 0.000 0.778 0.000 0.000 0.000
## el_s_:_LOSS 0.000 -0.715 0.000 0.000 0.000 -0.556
## el_s_:_WARM 0.000 -0.810 0.000 0.000 0.000 -0.630 0.579
## itt__:_LOSS -0.501 0.000 -0.637 -0.011 0.409 0.000 0.000 0.000
## itt__:_WARM -0.562 0.000 -0.714 0.408 0.273 0.000 0.000 0.000 0.455
## e__:__:_LOS 0.000 -0.495 0.000 0.000 0.000 -0.636 -0.034 0.401 0.000
## e__:__:_WAR 0.000 -0.550 0.000 0.000 0.000 -0.707 0.393 0.252 0.000
## i__:_W e__:__:_L
## eli_slf_pmc
## itt_cmp_gmc
## trgt_cnLOSS
## trgt_cnWARM
## el_slf_p:__
## el_s_:_LOSS
## el_s_:_WARM
## itt__:_LOSS
## itt__:_WARM
## e__:__:_LOS 0.000
## e__:__:_WAR 0.000 0.450
tab_model(compcond_eli_randslopes,
digits = 3)
| eli_targ_pmc | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.000 | -0.082 – 0.082 | 1.000 |
| eli self pmc | 0.088 | 0.009 – 0.168 | 0.029 |
| itt comp gmc | 0.000 | -0.065 – 0.065 | 1.000 |
| target condition [LOSS] | -0.000 | -0.112 – 0.112 | 1.000 |
| target condition [WARM] | 0.000 | -0.100 – 0.100 | 1.000 |
|
eli self pmc * itt comp gmc |
-0.050 | -0.113 – 0.013 | 0.122 |
|
eli self pmc * target condition [LOSS] |
-0.119 | -0.230 – -0.008 | 0.036 |
|
eli self pmc * target condition [WARM] |
-0.104 | -0.202 – -0.006 | 0.038 |
|
itt comp gmc * target condition [LOSS] |
0.000 | -0.102 – 0.102 | 1.000 |
|
itt comp gmc * target condition [WARM] |
0.000 | -0.091 – 0.091 | 1.000 |
|
(eli self pmc * itt comp gmc) * target condition [LOSS] |
-0.054 | -0.153 – 0.046 | 0.291 |
|
(eli self pmc * itt comp gmc) * target condition [WARM] |
0.054 | -0.035 – 0.144 | 0.237 |
| Random Effects | |||
| σ2 | 1.00 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.05 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.019 / 0.072 | ||
targ_levels <-list(target_condition = c("CONTROL", "LOSS", "WARM"))
simpslopes_eli_nostereo_compcond <- emtrends(compcond_eli_randslopes, ~ itt_comp_gmc*target_condition,
var ="eli_self_pmc",
at = c(targ_levels, threat_levels))
simpslopes_eli_nostereo_compcond
## itt_comp_gmc target_condition eli_self_pmc.trend SE df asymp.LCL
## -1.07 CONTROL 0.1418 0.0256 Inf 0.09158
## 0.00 CONTROL 0.0885 0.0405 Inf 0.00909
## 1.07 CONTROL 0.0351 0.0708 Inf -0.10354
## -1.07 LOSS 0.0804 0.0755 Inf -0.06760
## 0.00 LOSS -0.0302 0.0396 Inf -0.10786
## 1.07 LOSS -0.1409 0.0306 Inf -0.20078
## -1.07 WARM -0.0200 0.0548 Inf -0.12736
## 0.00 WARM -0.0155 0.0294 Inf -0.07311
## 1.07 WARM -0.0111 0.0334 Inf -0.07645
## asymp.UCL
## 0.1921
## 0.1679
## 0.1738
## 0.2284
## 0.0474
## -0.0809
## 0.0874
## 0.0420
## 0.0543
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
pairs(simpslopes_eli_nostereo_compcond)
## contrast estimate SE df z.ratio p.value
## (-1.07 CONTROL) - 0 CONTROL 0.05334 0.0345 Inf 1.546 0.8338
## (-1.07 CONTROL) - 1.07 CONTROL 0.10669 0.0690 Inf 1.546 0.8338
## (-1.07 CONTROL) - (-1.07 LOSS) 0.06142 0.0797 Inf 0.770 0.9976
## (-1.07 CONTROL) - 0 LOSS 0.17205 0.0472 Inf 3.647 0.0081
## (-1.07 CONTROL) - 1.07 LOSS 0.28269 0.0399 Inf 7.085 <.0001
## (-1.07 CONTROL) - (-1.07 WARM) 0.16183 0.0605 Inf 2.676 0.1564
## (-1.07 CONTROL) - 0 WARM 0.15736 0.0390 Inf 4.036 0.0018
## (-1.07 CONTROL) - 1.07 WARM 0.15289 0.0421 Inf 3.634 0.0085
## 0 CONTROL - 1.07 CONTROL 0.05334 0.0345 Inf 1.546 0.8338
## 0 CONTROL - (-1.07 LOSS) 0.00807 0.0857 Inf 0.094 1.0000
## 0 CONTROL - 0 LOSS 0.11871 0.0567 Inf 2.095 0.4764
## 0 CONTROL - 1.07 LOSS 0.22934 0.0507 Inf 4.519 0.0002
## 0 CONTROL - (-1.07 WARM) 0.10848 0.0681 Inf 1.592 0.8094
## 0 CONTROL - 0 WARM 0.10402 0.0500 Inf 2.079 0.4879
## 0 CONTROL - 1.07 WARM 0.09955 0.0525 Inf 1.897 0.6157
## 1.07 CONTROL - (-1.07 LOSS) -0.04527 0.1035 Inf -0.437 1.0000
## 1.07 CONTROL - 0 LOSS 0.06537 0.0811 Inf 0.806 0.9967
## 1.07 CONTROL - 1.07 LOSS 0.17600 0.0771 Inf 2.283 0.3521
## 1.07 CONTROL - (-1.07 WARM) 0.05514 0.0895 Inf 0.616 0.9995
## 1.07 CONTROL - 0 WARM 0.05067 0.0766 Inf 0.661 0.9992
## 1.07 CONTROL - 1.07 WARM 0.04621 0.0782 Inf 0.591 0.9997
## (-1.07 LOSS) - 0 LOSS 0.11063 0.0418 Inf 2.645 0.1683
## (-1.07 LOSS) - 1.07 LOSS 0.22127 0.0837 Inf 2.645 0.1683
## (-1.07 LOSS) - (-1.07 WARM) 0.10041 0.0933 Inf 1.076 0.9776
## (-1.07 LOSS) - 0 WARM 0.09594 0.0810 Inf 1.184 0.9600
## (-1.07 LOSS) - 1.07 WARM 0.09148 0.0826 Inf 1.108 0.9732
## 0 LOSS - 1.07 LOSS 0.11063 0.0418 Inf 2.645 0.1683
## 0 LOSS - (-1.07 WARM) -0.01023 0.0676 Inf -0.151 1.0000
## 0 LOSS - 0 WARM -0.01469 0.0493 Inf -0.298 1.0000
## 0 LOSS - 1.07 WARM -0.01916 0.0518 Inf -0.370 1.0000
## 1.07 LOSS - (-1.07 WARM) -0.12086 0.0627 Inf -1.927 0.5951
## 1.07 LOSS - 0 WARM -0.12533 0.0424 Inf -2.956 0.0760
## 1.07 LOSS - 1.07 WARM -0.12979 0.0452 Inf -2.868 0.0964
## (-1.07 WARM) - 0 WARM -0.00447 0.0346 Inf -0.129 1.0000
## (-1.07 WARM) - 1.07 WARM -0.00893 0.0691 Inf -0.129 1.0000
## 0 WARM - 1.07 WARM -0.00447 0.0346 Inf -0.129 1.0000
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 9 estimates
# checking normality of conditional residuals
qqnorm(residuals(compcond_eli_randslopes), main="Q-Q plot for conditional residuals")
# checking the normality of the random effects
qqnorm(ranef(compcond_eli_randslopes)$sub_id$eli_self_pmc,
main="Q-Q plot for the self random effect")
plot_model(compcond_eli_randslopes, type='diag')
## [[1]]
##
## [[2]]
## [[2]]$sub_id
##
##
## [[3]]
##
## [[4]]
Heavy tail?
Also seems evenly spread but diagonal
eli_nostereo_int <- effect("eli_self_pmc:itt_comp_gmc:target_condition",
xlevels = list(itt_comp_gmc = c(-1.07, 0.0, 1.07),
target_condition = c("CONTROL",
"WARM",
"LOSS")),
mod = compcond_eli_randslopes)
eli_nostereo_int <- as.data.frame(eli_nostereo_int)
eli_nostereo_int$itt_comp_gmc <- as.factor(eli_nostereo_int$itt_comp_gmc)
eli_nostereo_int$target_condition <- as.factor(eli_nostereo_int$target_condition)
eli_nostereo_int %<>%
mutate(target_condition = forcats::fct_relevel(target_condition, c("CONTROL", "WARM", "LOSS")))
target_labels <- c("CONTROL" = "Control",
"WARM" = "Warm",
"LOSS" = "Loss")
ggplot(eli_nostereo_int, aes(eli_self_pmc, fit, group = itt_comp_gmc)) +
geom_smooth(method = "lm",
size = .7,
se = FALSE,
colour = "black",
aes(linetype = itt_comp_gmc)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
facet_wrap(~target_condition,
labeller = labeller(target_condition = target_labels)) +
scale_linetype_manual("Threat composite",
breaks = c("-1.07", "0", "1.07"),
labels = c("Low",
"Average",
"High"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Projection by target-level threat and target condition",
subtitle = "Accounting for stereotyping",
x = "ELI responses for self",
y = "ELI responses for target")
threat_descrip <- describe(eli_data$itt_comp_gmc)
onesdlow <- threat_descrip$mean - threat_descrip$sd
onesdlow
## [1] -1.070839
onesdhigh <- threat_descrip$mean + threat_descrip$sd
onesdhigh
## [1] 1.070839
eli_descrip <- eli_data %>%
select(sub_id, eli_number, eli_targ_pmc, eli_self_pmc, target_condition, itt_comp_gmc) %>%
mutate(itt_comp_cat = as.factor(ifelse(itt_comp_gmc < onesdlow, "Low",
ifelse(itt_comp_gmc >= onesdlow & itt_comp_gmc <= onesdhigh, "Ave",
ifelse(itt_comp_gmc > onesdhigh, "High", NA)))))
# group_by(bfi_self_pmc, itt_comp_gmc, target_condition) %>%
# mutate(mean = mean(bfi_targ_pmc),
# sd = sd(bfi_targ_pmc))
eli_descrip %<>%
mutate(target_condition = forcats::fct_relevel(target_condition, c("CONTROL", "WARM", "LOSS")))
ggplot(eli_descrip, aes(eli_self_pmc, eli_targ_pmc, group = itt_comp_cat)) +
geom_smooth(method = "lm",
size = .7,
colour = "black",
se = FALSE,
aes(linetype = itt_comp_cat)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
facet_wrap(~target_condition,
labeller = labeller(target_condition = target_labels))
filter_warmlow <- eli_data %>%
filter(target_condition == "WARM" &
itt_comp_gmc < -1.07)
hist(filter_warmlow$eli_self_pmc)
hist(filter_warmlow$eli_targ_pmc)
These are different… corresponds to why I am seeing counter-projection, but just doesn’t make sense.
filter_warmave <- eli_data %>%
filter(target_condition == "WARM" &
itt_comp_gmc > -1.07 & itt_comp_gmc < 1.07)
hist(filter_warmave$eli_self_pmc)
hist(filter_warmave$eli_targ_pmc)
These are pretty similiar, which corresponds to why we are seeing projection
filter_warmhigh <- eli_data %>%
filter(target_condition == "WARM" &
itt_comp_gmc > 1.07)
hist(filter_warmhigh$eli_self_pmc)
hist(filter_warmhigh$eli_targ_pmc)
Different enough that I’m not suprised we are seeing projection, but is less different than with the low threat… which just seems so weird
analogcomp_eli_randslopes <- lmer(eli_targ ~ eli_self_pmc*analog_condition*itt_comp_gmc +
(0 + eli_self_pmc | sub_id), data = eli_data)
summary(analogcomp_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ ~ eli_self_pmc * analog_condition * itt_comp_gmc + (0 +
## eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12722.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.51750 -0.68352 -0.07376 0.76725 2.57997
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.0289 0.170
## Residual 1.1260 1.061
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.127608 0.023557 132.767
## eli_self_pmc 0.004498 0.022279 0.202
## analog_conditioncontrol 0.035595 0.032696 1.089
## itt_comp_gmc -0.118886 0.022344 -5.321
## eli_self_pmc:analog_conditioncontrol 0.020146 0.030791 0.654
## eli_self_pmc:itt_comp_gmc -0.059057 0.020877 -2.829
## analog_conditioncontrol:itt_comp_gmc 0.042211 0.030584 1.380
## eli_self_pmc:analog_conditioncontrol:itt_comp_gmc -0.051639 0.028680 -1.801
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ anlg_c itt_c_ el__:_ e__:__ an_:__
## eli_slf_pmc 0.000
## anlg_cndtnc -0.721 0.000
## itt_cmp_gmc 0.073 0.000 -0.053
## el_slf_pm:_ 0.000 -0.724 0.000 0.000
## el_slf_p:__ 0.000 0.047 0.000 0.000 -0.034
## anlg_cnd:__ -0.053 0.000 0.007 -0.731 0.000 0.000
## el_sl_:_:__ 0.000 -0.034 0.000 0.000 -0.012 -0.728 0.000
tab_model(analogcomp_eli_randslopes)
| eli_targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.13 | 3.08 – 3.17 | <0.001 |
| eli self pmc | 0.00 | -0.04 – 0.05 | 0.840 |
|
analog condition [control] |
0.04 | -0.03 – 0.10 | 0.276 |
| itt comp gmc | -0.12 | -0.16 – -0.08 | <0.001 |
|
eli self pmc * analog condition [control] |
0.02 | -0.04 – 0.08 | 0.513 |
|
eli self pmc * itt comp gmc |
-0.06 | -0.10 – -0.02 | 0.005 |
|
analog condition [control] * itt comp gmc |
0.04 | -0.02 – 0.10 | 0.168 |
|
(eli self pmc * analog condition [control]) * itt comp gmc |
-0.05 | -0.11 – 0.00 | 0.072 |
| Random Effects | |||
| σ2 | 1.13 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.04 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.022 / 0.063 | ||
analogcond_eli_randslopes <- lmer(eli_targ ~ eli_self_pmc*analog_condition*target_condition +
(0 + eli_self_pmc | sub_id), data = eli_data)
summary(analogcond_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ ~ eli_self_pmc * analog_condition * target_condition +
## (0 + eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12695.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.46976 -0.62515 -0.04895 0.75378 2.64903
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.03014 0.1736
## Residual 1.11583 1.0563
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 3.3126761 0.0396434
## eli_self_pmc 0.1419875 0.0380866
## analog_conditioncontrol -0.0226761 0.0544646
## target_conditionLOSS -0.3439261 0.0575768
## target_conditionWARM -0.2010819 0.0564690
## eli_self_pmc:analog_conditioncontrol -0.0092576 0.0518635
## eli_self_pmc:target_conditionLOSS -0.2487041 0.0552807
## eli_self_pmc:target_conditionWARM -0.1654422 0.0538018
## analog_conditioncontrol:target_conditionLOSS -0.0007614 0.0803329
## analog_conditioncontrol:target_conditionWARM 0.1084503 0.0777930
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 0.0120479 0.0762011
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 0.0266920 0.0740320
## t value
## (Intercept) 83.562
## eli_self_pmc 3.728
## analog_conditioncontrol -0.416
## target_conditionLOSS -5.973
## target_conditionWARM -3.561
## eli_self_pmc:analog_conditioncontrol -0.178
## eli_self_pmc:target_conditionLOSS -4.499
## eli_self_pmc:target_conditionWARM -3.075
## analog_conditioncontrol:target_conditionLOSS -0.009
## analog_conditioncontrol:target_conditionWARM 1.394
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 0.158
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 0.361
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ anlg_c t_LOSS t_WARM el__:_ e__:_L e__:_W a_:_LO
## eli_slf_pmc 0.000
## anlg_cndtnc -0.728 0.000
## trgt_cnLOSS -0.689 0.000 0.501
## trgt_cnWARM -0.702 0.000 0.511 0.483
## el_slf_pm:_ 0.000 -0.734 0.000 0.000 0.000
## el_s_:_LOSS 0.000 -0.689 0.000 0.000 0.000 0.506
## el_s_:_WARM 0.000 -0.708 0.000 0.000 0.000 0.520 0.488
## anlg_:_LOSS 0.493 0.000 -0.678 -0.717 -0.346 0.000 0.000 0.000
## anlg_:_WARM 0.510 0.000 -0.700 -0.351 -0.726 0.000 0.000 0.000 0.475
## e__:_:_LOSS 0.000 0.500 0.000 0.000 0.000 -0.681 -0.725 -0.354 0.000
## e__:_:_WARM 0.000 0.514 0.000 0.000 0.000 -0.701 -0.354 -0.727 0.000
## a_:_WA e__:_:_L
## eli_slf_pmc
## anlg_cndtnc
## trgt_cnLOSS
## trgt_cnWARM
## el_slf_pm:_
## el_s_:_LOSS
## el_s_:_WARM
## anlg_:_LOSS
## anlg_:_WARM
## e__:_:_LOSS 0.000
## e__:_:_WARM 0.000 0.477
tab_model(analogcond_eli_randslopes)
| eli_targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.31 | 3.23 – 3.39 | <0.001 |
| eli self pmc | 0.14 | 0.07 – 0.22 | <0.001 |
|
analog condition [control] |
-0.02 | -0.13 – 0.08 | 0.677 |
| target condition [LOSS] | -0.34 | -0.46 – -0.23 | <0.001 |
| target condition [WARM] | -0.20 | -0.31 – -0.09 | <0.001 |
|
eli self pmc * analog condition [control] |
-0.01 | -0.11 – 0.09 | 0.858 |
|
eli self pmc * target condition [LOSS] |
-0.25 | -0.36 – -0.14 | <0.001 |
|
eli self pmc * target condition [WARM] |
-0.17 | -0.27 – -0.06 | 0.002 |
|
analog condition [control] * target condition [LOSS] |
-0.00 | -0.16 – 0.16 | 0.992 |
|
analog condition [control] * target condition [WARM] |
0.11 | -0.04 – 0.26 | 0.163 |
|
(eli self pmc * analog condition [control]) * target condition [LOSS] |
0.01 | -0.14 – 0.16 | 0.874 |
|
(eli self pmc * analog condition [control]) * target condition [WARM] |
0.03 | -0.12 – 0.17 | 0.718 |
| Random Effects | |||
| σ2 | 1.12 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.04 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.031 / 0.072 | ||
analogcompcond_eli_randslopes <- lmer(eli_targ ~ eli_self_pmc*analog_condition*target_condition*itt_comp_gmc +
(0 + eli_self_pmc | sub_id), data = eli_data)
summary(analogcompcond_eli_randslopes)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ ~ eli_self_pmc * analog_condition * target_condition *
## itt_comp_gmc + (0 + eli_self_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 12716.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.45864 -0.62278 -0.04065 0.74439 2.67835
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_id eli_self_pmc 0.02633 0.1623
## Residual 1.11580 1.0563
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate
## (Intercept) 3.270334
## eli_self_pmc 0.164799
## analog_conditioncontrol 0.074945
## target_conditionLOSS -0.265588
## target_conditionWARM -0.147308
## itt_comp_gmc -0.042857
## eli_self_pmc:analog_conditioncontrol -0.145364
## eli_self_pmc:target_conditionLOSS -0.237944
## eli_self_pmc:target_conditionWARM -0.194914
## analog_conditioncontrol:target_conditionLOSS -0.033859
## analog_conditioncontrol:target_conditionWARM -0.012297
## eli_self_pmc:itt_comp_gmc 0.023238
## analog_conditioncontrol:itt_comp_gmc 0.098220
## target_conditionLOSS:itt_comp_gmc -0.011096
## target_conditionWARM:itt_comp_gmc -0.024346
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 0.235676
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 0.197303
## eli_self_pmc:analog_conditioncontrol:itt_comp_gmc -0.140308
## eli_self_pmc:target_conditionLOSS:itt_comp_gmc -0.068673
## eli_self_pmc:target_conditionWARM:itt_comp_gmc 0.009972
## analog_conditioncontrol:target_conditionLOSS:itt_comp_gmc -0.182028
## analog_conditioncontrol:target_conditionWARM:itt_comp_gmc -0.012837
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS:itt_comp_gmc 0.020668
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM:itt_comp_gmc 0.064131
## Std. Error
## (Intercept) 0.062646
## eli_self_pmc 0.058854
## analog_conditioncontrol 0.088192
## target_conditionLOSS 0.085502
## target_conditionWARM 0.074871
## itt_comp_gmc 0.049097
## eli_self_pmc:analog_conditioncontrol 0.080668
## eli_self_pmc:target_conditionLOSS 0.081249
## eli_self_pmc:target_conditionWARM 0.070077
## analog_conditioncontrol:target_conditionLOSS 0.121480
## analog_conditioncontrol:target_conditionWARM 0.110388
## eli_self_pmc:itt_comp_gmc 0.046165
## analog_conditioncontrol:itt_comp_gmc 0.069832
## target_conditionLOSS:itt_comp_gmc 0.078109
## target_conditionWARM:itt_comp_gmc 0.067990
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 0.112743
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 0.101900
## eli_self_pmc:analog_conditioncontrol:itt_comp_gmc 0.064158
## eli_self_pmc:target_conditionLOSS:itt_comp_gmc 0.071769
## eli_self_pmc:target_conditionWARM:itt_comp_gmc 0.063810
## analog_conditioncontrol:target_conditionLOSS:itt_comp_gmc 0.109736
## analog_conditioncontrol:target_conditionWARM:itt_comp_gmc 0.100645
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS:itt_comp_gmc 0.100659
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM:itt_comp_gmc 0.093574
## t value
## (Intercept) 52.203
## eli_self_pmc 2.800
## analog_conditioncontrol 0.850
## target_conditionLOSS -3.106
## target_conditionWARM -1.967
## itt_comp_gmc -0.873
## eli_self_pmc:analog_conditioncontrol -1.802
## eli_self_pmc:target_conditionLOSS -2.929
## eli_self_pmc:target_conditionWARM -2.781
## analog_conditioncontrol:target_conditionLOSS -0.279
## analog_conditioncontrol:target_conditionWARM -0.111
## eli_self_pmc:itt_comp_gmc 0.503
## analog_conditioncontrol:itt_comp_gmc 1.407
## target_conditionLOSS:itt_comp_gmc -0.142
## target_conditionWARM:itt_comp_gmc -0.358
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS 2.090
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM 1.936
## eli_self_pmc:analog_conditioncontrol:itt_comp_gmc -2.187
## eli_self_pmc:target_conditionLOSS:itt_comp_gmc -0.957
## eli_self_pmc:target_conditionWARM:itt_comp_gmc 0.156
## analog_conditioncontrol:target_conditionLOSS:itt_comp_gmc -1.659
## analog_conditioncontrol:target_conditionWARM:itt_comp_gmc -0.128
## eli_self_pmc:analog_conditioncontrol:target_conditionLOSS:itt_comp_gmc 0.205
## eli_self_pmc:analog_conditioncontrol:target_conditionWARM:itt_comp_gmc 0.685
tab_model(analogcompcond_eli_randslopes)
| eli_targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.27 | 3.15 – 3.39 | <0.001 |
| eli self pmc | 0.16 | 0.05 – 0.28 | 0.005 |
|
analog condition [control] |
0.07 | -0.10 – 0.25 | 0.395 |
| target condition [LOSS] | -0.27 | -0.43 – -0.10 | 0.002 |
| target condition [WARM] | -0.15 | -0.29 – -0.00 | 0.049 |
| itt comp gmc | -0.04 | -0.14 – 0.05 | 0.383 |
|
eli self pmc * analog condition [control] |
-0.15 | -0.30 – 0.01 | 0.072 |
|
eli self pmc * target condition [LOSS] |
-0.24 | -0.40 – -0.08 | 0.003 |
|
eli self pmc * target condition [WARM] |
-0.19 | -0.33 – -0.06 | 0.005 |
|
analog condition [control] * target condition [LOSS] |
-0.03 | -0.27 – 0.20 | 0.780 |
|
analog condition [control] * target condition [WARM] |
-0.01 | -0.23 – 0.20 | 0.911 |
|
eli self pmc * itt comp gmc |
0.02 | -0.07 – 0.11 | 0.615 |
|
analog condition [control] * itt comp gmc |
0.10 | -0.04 – 0.24 | 0.160 |
|
target condition [LOSS] * itt comp gmc |
-0.01 | -0.16 – 0.14 | 0.887 |
|
target condition [WARM] * itt comp gmc |
-0.02 | -0.16 – 0.11 | 0.720 |
|
(eli self pmc * analog condition [control]) * target condition [LOSS] |
0.24 | 0.01 – 0.46 | 0.037 |
|
(eli self pmc * analog condition [control]) * target condition [WARM] |
0.20 | -0.00 – 0.40 | 0.053 |
|
(eli self pmc * analog condition [control]) * itt comp gmc |
-0.14 | -0.27 – -0.01 | 0.029 |
|
(eli self pmc * target condition [LOSS]) * itt comp gmc |
-0.07 | -0.21 – 0.07 | 0.339 |
|
(eli self pmc * target condition [WARM]) * itt comp gmc |
0.01 | -0.12 – 0.14 | 0.876 |
|
(analog condition [control] * target condition [LOSS]) * itt comp gmc |
-0.18 | -0.40 – 0.03 | 0.097 |
|
(analog condition [control] * target condition [WARM]) * itt comp gmc |
-0.01 | -0.21 – 0.18 | 0.899 |
|
(eli self pmc * analog condition [control] target condition [LOSS]) itt comp gmc |
0.02 | -0.18 – 0.22 | 0.837 |
|
(eli self pmc * analog condition [control] target condition [WARM]) itt comp gmc |
0.06 | -0.12 – 0.25 | 0.493 |
| Random Effects | |||
| σ2 | 1.12 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| ρ01 | |||
| ρ01 | |||
| ICC | 0.04 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.039 / 0.075 | ||
The interaction significant in the 4-way is not in the 3-way model; it could be because of variance removed due to itt_comp… or it could be because the model is overfit/underpowered with this number of predictors. I’m not sure I trust ANY effect with analog perspective-taking
cor_predictors_eli <- clean_data_eli %>%
select(sub_id, eli_number, eli_self, eli_targ, itt_comp) %>%
unique() %>%
na.omit() %>%
select(eli_self, eli_targ, itt_comp) %>%
rename("ELI: Self" = eli_self,
"ELI: Target" = eli_targ,
"Threat Composite" = itt_comp)
cor_matrix_predictors_eli <- cor(cor_predictors_eli)
corrplot(cor_matrix_predictors_eli,
is.corr = TRUE,
#method = "number",
method = 'color',
tl.cex = .85,
tl.col = 'black',
addgrid.col = 'white',
addCoef.col = 'grey50')
comp_eli_stereo <- lmer(eli_targ_pmc ~ eli_self_pmc*itt_comp_gmc*eli_stereo_pmc + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc + eli_stereo_pmc | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(comp_eli_stereo)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc * itt_comp_gmc * eli_stereo_pmc +
## (0 + eli_self_pmc + eli_stereo_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 11416.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6808 -0.5355 0.0171 0.5988 3.4913
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_id eli_self_pmc 0.02599 0.1612
## eli_stereo_pmc 0.07400 0.2720 -0.01
## Residual 0.75865 0.8710
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0011854 0.0134003 0.088
## eli_self_pmc 0.0275174 0.0136422 2.017
## itt_comp_gmc 0.0004259 0.0125165 0.034
## eli_stereo_pmc 0.2737683 0.0187626 14.591
## eli_self_pmc:itt_comp_gmc -0.0615540 0.0127172 -4.840
## eli_self_pmc:eli_stereo_pmc 0.0047924 0.0089489 0.536
## itt_comp_gmc:eli_stereo_pmc 0.1379914 0.0174409 7.912
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc 0.0120902 0.0081329 1.487
##
## Correlation of Fixed Effects:
## (Intr) el_sl_ itt_c_ el_st_ el_slf_pmc:t__ el_slf_pmc:l__ i__:__
## eli_slf_pmc 0.000
## itt_cmp_gmc 0.000 0.003
## eli_str_pmc -0.002 -0.012 0.000
## el_slf_pmc:t__ 0.003 -0.020 0.000 0.031
## el_slf_pmc:l__ -0.006 0.050 0.061 -0.004 -0.006
## itt_cmp_:__ 0.000 0.030 -0.002 -0.038 -0.012 -0.026
## el_s_:__:__ 0.060 -0.007 -0.005 -0.025 0.045 -0.097 -0.001
tab_model(comp_eli_stereo,
digits = 3)
| eli_targ_pmc | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.001 | -0.025 – 0.027 | 0.930 |
| eli self pmc | 0.028 | 0.001 – 0.054 | 0.044 |
| itt comp gmc | 0.000 | -0.024 – 0.025 | 0.973 |
| eli stereo pmc | 0.274 | 0.237 – 0.311 | <0.001 |
|
eli self pmc * itt comp gmc |
-0.062 | -0.086 – -0.037 | <0.001 |
|
eli self pmc * eli stereo pmc |
0.005 | -0.013 – 0.022 | 0.592 |
|
itt comp gmc * eli stereo pmc |
0.138 | 0.104 – 0.172 | <0.001 |
|
(eli self pmc * itt comp gmc) * eli stereo pmc |
0.012 | -0.004 – 0.028 | 0.137 |
| Random Effects | |||
| σ2 | 0.76 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.03 | ||
| τ11 sub_id.eli_stereo_pmc | 0.07 | ||
| ρ01 sub_id | -0.01 | ||
| ICC | 0.16 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.149 / 0.287 | ||
comp_stereo_simpslopes_eli <- emtrends(comp_eli_stereo, ~ itt_comp_gmc,
var ="eli_self_pmc",
at = threat_levels)
comp_stereo_simpslopes_eli
## itt_comp_gmc eli_self_pmc.trend SE df asymp.LCL asymp.UCL
## -1.07 0.0934 0.0195 Inf 0.055240 0.131520
## 0.00 0.0275 0.0136 Inf 0.000779 0.054256
## 1.07 -0.0383 0.0191 Inf -0.075732 -0.000958
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
comp_stereo_eli_maineffect <- effect("eli_self_pmc:itt_comp_gmc",
xlevels = list(itt_comp_gmc = c(-1.07, 0, 1.07)),
mod = comp_eli_stereo)
comp_stereo_eli_maineffect <- as.data.frame(comp_stereo_eli_maineffect)
comp_stereo_eli_maineffect$itt_comp_gmc <- as.factor(comp_stereo_eli_maineffect$itt_comp_gmc)
ggplot(comp_stereo_eli_maineffect, aes(eli_self_pmc, fit, group = itt_comp_gmc)) +
geom_smooth(method = "lm",
size = .7,
se = FALSE,
colour = "black",
aes(linetype = itt_comp_gmc)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
scale_linetype_manual("Target-level threat",
breaks = c(-1.07, 0, 1.07),
labels = c("Low",
"Average",
"High"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Projection by target-level threat",
subtitle = "Using the ELI",
x = "ELI responses for self",
y = "ELI responses for target")
# checking normality of conditional residuals
qqnorm(residuals(comp_eli_stereo), main="Q-Q plot for conditional residuals")
# checking the normality of the random effects:
qqnorm(ranef(comp_eli_stereo)$sub_id$eli_self_pmc,
main="Q-Q plot for the self random effect")
# looking at random effect for stereo:
qqnorm(ranef(comp_eli_stereo)$sub_id$eli_stereo_pmc,
main="Q-Q plot for the self random effect")
plot_model(comp_eli_stereo, type='diag')
## [[1]]
##
## [[2]]
## [[2]]$sub_id
##
##
## [[3]]
##
## [[4]]
Heavy tail and outliers?
compcond_eli_stereo <- lmer(eli_targ_pmc ~ eli_self_pmc*itt_comp_gmc*eli_stereo_pmc*target_condition + # itt does not work as a RE; model does not converge
(0 + eli_self_pmc + eli_stereo_pmc | sub_id),
data = eli_data) # Same as above, works with clean_data but not the smaller df specific to this analysis
summary(compcond_eli_stereo)
## Linear mixed model fit by REML ['lmerMod']
## Formula: eli_targ_pmc ~ eli_self_pmc * itt_comp_gmc * eli_stereo_pmc *
## target_condition + (0 + eli_self_pmc + eli_stereo_pmc | sub_id)
## Data: eli_data
##
## REML criterion at convergence: 11425.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7121 -0.5415 0.0197 0.6013 3.3662
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_id eli_self_pmc 0.02356 0.1535
## eli_stereo_pmc 0.06033 0.2456 0.14
## Residual 0.75930 0.8714
## Number of obs: 4240, groups: sub_id, 424
##
## Fixed effects:
## Estimate
## (Intercept) 0.0014845
## eli_self_pmc 0.1028476
## itt_comp_gmc 0.0005146
## eli_stereo_pmc 0.1359810
## target_conditionLOSS -0.0045587
## target_conditionWARM -0.0007657
## eli_self_pmc:itt_comp_gmc -0.0325643
## eli_self_pmc:eli_stereo_pmc 0.0068278
## itt_comp_gmc:eli_stereo_pmc 0.0584795
## eli_self_pmc:target_conditionLOSS -0.1626245
## eli_self_pmc:target_conditionWARM -0.1117977
## itt_comp_gmc:target_conditionLOSS 0.0065993
## itt_comp_gmc:target_conditionWARM -0.0010538
## eli_stereo_pmc:target_conditionLOSS 0.3385465
## eli_stereo_pmc:target_conditionWARM 0.1129196
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc 0.0138602
## eli_self_pmc:itt_comp_gmc:target_conditionLOSS 0.0076406
## eli_self_pmc:itt_comp_gmc:target_conditionWARM 0.0568879
## eli_self_pmc:eli_stereo_pmc:target_conditionLOSS 0.0173524
## eli_self_pmc:eli_stereo_pmc:target_conditionWARM -0.0111602
## itt_comp_gmc:eli_stereo_pmc:target_conditionLOSS 0.0319973
## itt_comp_gmc:eli_stereo_pmc:target_conditionWARM -0.0168939
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc:target_conditionLOSS -0.0074514
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc:target_conditionWARM -0.0005625
## Std. Error
## (Intercept) 0.0364308
## eli_self_pmc 0.0358502
## itt_comp_gmc 0.0288578
## eli_stereo_pmc 0.0478002
## target_conditionLOSS 0.0508162
## target_conditionWARM 0.0446240
## eli_self_pmc:itt_comp_gmc 0.0285094
## eli_self_pmc:eli_stereo_pmc 0.0234383
## itt_comp_gmc:eli_stereo_pmc 0.0385392
## eli_self_pmc:target_conditionLOSS 0.0508035
## eli_self_pmc:target_conditionWARM 0.0442455
## itt_comp_gmc:target_conditionLOSS 0.0459587
## itt_comp_gmc:target_conditionWARM 0.0403926
## eli_stereo_pmc:target_conditionLOSS 0.0692498
## eli_stereo_pmc:target_conditionWARM 0.0587108
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc 0.0189388
## eli_self_pmc:itt_comp_gmc:target_conditionLOSS 0.0456204
## eli_self_pmc:itt_comp_gmc:target_conditionWARM 0.0403978
## eli_self_pmc:eli_stereo_pmc:target_conditionLOSS 0.0349865
## eli_self_pmc:eli_stereo_pmc:target_conditionWARM 0.0293039
## itt_comp_gmc:eli_stereo_pmc:target_conditionLOSS 0.0625333
## itt_comp_gmc:eli_stereo_pmc:target_conditionWARM 0.0529115
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc:target_conditionLOSS 0.0304248
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc:target_conditionWARM 0.0264207
## t value
## (Intercept) 0.041
## eli_self_pmc 2.869
## itt_comp_gmc 0.018
## eli_stereo_pmc 2.845
## target_conditionLOSS -0.090
## target_conditionWARM -0.017
## eli_self_pmc:itt_comp_gmc -1.142
## eli_self_pmc:eli_stereo_pmc 0.291
## itt_comp_gmc:eli_stereo_pmc 1.517
## eli_self_pmc:target_conditionLOSS -3.201
## eli_self_pmc:target_conditionWARM -2.527
## itt_comp_gmc:target_conditionLOSS 0.144
## itt_comp_gmc:target_conditionWARM -0.026
## eli_stereo_pmc:target_conditionLOSS 4.889
## eli_stereo_pmc:target_conditionWARM 1.923
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc 0.732
## eli_self_pmc:itt_comp_gmc:target_conditionLOSS 0.167
## eli_self_pmc:itt_comp_gmc:target_conditionWARM 1.408
## eli_self_pmc:eli_stereo_pmc:target_conditionLOSS 0.496
## eli_self_pmc:eli_stereo_pmc:target_conditionWARM -0.381
## itt_comp_gmc:eli_stereo_pmc:target_conditionLOSS 0.512
## itt_comp_gmc:eli_stereo_pmc:target_conditionWARM -0.319
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc:target_conditionLOSS -0.245
## eli_self_pmc:itt_comp_gmc:eli_stereo_pmc:target_conditionWARM -0.021
tab_model(compcond_eli_stereo,
digits = 3)
| eli_targ_pmc | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.001 | -0.070 – 0.073 | 0.967 |
| eli self pmc | 0.103 | 0.033 – 0.173 | 0.004 |
| itt comp gmc | 0.001 | -0.056 – 0.057 | 0.986 |
| eli stereo pmc | 0.136 | 0.042 – 0.230 | 0.004 |
| target condition [LOSS] | -0.005 | -0.104 – 0.095 | 0.929 |
| target condition [WARM] | -0.001 | -0.088 – 0.087 | 0.986 |
|
eli self pmc * itt comp gmc |
-0.033 | -0.088 – 0.023 | 0.253 |
|
eli self pmc * eli stereo pmc |
0.007 | -0.039 – 0.053 | 0.771 |
|
itt comp gmc * eli stereo pmc |
0.058 | -0.017 – 0.134 | 0.129 |
|
eli self pmc * target condition [LOSS] |
-0.163 | -0.262 – -0.063 | 0.001 |
|
eli self pmc * target condition [WARM] |
-0.112 | -0.199 – -0.025 | 0.012 |
|
itt comp gmc * target condition [LOSS] |
0.007 | -0.084 – 0.097 | 0.886 |
|
itt comp gmc * target condition [WARM] |
-0.001 | -0.080 – 0.078 | 0.979 |
|
eli stereo pmc * target condition [LOSS] |
0.339 | 0.203 – 0.474 | <0.001 |
|
eli stereo pmc * target condition [WARM] |
0.113 | -0.002 – 0.228 | 0.055 |
|
(eli self pmc * itt comp gmc) * eli stereo pmc |
0.014 | -0.023 – 0.051 | 0.464 |
|
(eli self pmc * itt comp gmc) * target condition [LOSS] |
0.008 | -0.082 – 0.097 | 0.867 |
|
(eli self pmc * itt comp gmc) * target condition [WARM] |
0.057 | -0.022 – 0.136 | 0.159 |
|
(eli self pmc * eli stereo pmc) * target condition [LOSS] |
0.017 | -0.051 – 0.086 | 0.620 |
|
(eli self pmc * eli stereo pmc) * target condition [WARM] |
-0.011 | -0.069 – 0.046 | 0.703 |
|
(itt comp gmc * eli stereo pmc) * target condition [LOSS] |
0.032 | -0.091 – 0.155 | 0.609 |
|
(itt comp gmc * eli stereo pmc) * target condition [WARM] |
-0.017 | -0.121 – 0.087 | 0.750 |
|
(eli self pmc * itt comp gmc * eli stereo pmc) * target condition [LOSS] |
-0.007 | -0.067 – 0.052 | 0.807 |
|
(eli self pmc * itt comp gmc * eli stereo pmc) * target condition [WARM] |
-0.001 | -0.052 – 0.051 | 0.983 |
| Random Effects | |||
| σ2 | 0.76 | ||
| τ00 | |||
| τ00 | |||
| τ11 sub_id.eli_self_pmc | 0.02 | ||
| τ11 sub_id.eli_stereo_pmc | 0.06 | ||
| ρ01 sub_id | 0.14 | ||
| ICC | 0.14 | ||
| N sub_id | 424 | ||
| Observations | 4240 | ||
| Marginal R2 / Conditional R2 | 0.173 / 0.288 | ||
Slight dip to the left but relatively spread, not funnel shape
simpslopes_eli_stereo_compcond <- emtrends(compcond_eli_stereo, ~ itt_comp_gmc*target_condition,
var ="eli_self_pmc",
at = c(targ_levels, threat_levels))
simpslopes_eli_stereo_compcond
## itt_comp_gmc target_condition eli_self_pmc.trend SE df asymp.LCL
## -1.07 CONTROL 0.13769 0.0226 Inf 0.0934
## 0.00 CONTROL 0.10285 0.0359 Inf 0.0326
## 1.07 CONTROL 0.06800 0.0626 Inf -0.0547
## -1.07 LOSS -0.03311 0.0691 Inf -0.1685
## 0.00 LOSS -0.05978 0.0360 Inf -0.1303
## 1.07 LOSS -0.08645 0.0269 Inf -0.1392
## -1.07 WARM -0.03498 0.0484 Inf -0.1299
## 0.00 WARM -0.00895 0.0259 Inf -0.0598
## 1.07 WARM 0.01708 0.0296 Inf -0.0409
## asymp.UCL
## 0.1820
## 0.1731
## 0.1907
## 0.1023
## 0.0108
## -0.0337
## 0.0599
## 0.0419
## 0.0751
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
pairs(simpslopes_eli_stereo_compcond)
## contrast estimate SE df z.ratio p.value
## (-1.07 CONTROL) - 0 CONTROL 0.03484 0.0305 Inf 1.142 0.9678
## (-1.07 CONTROL) - 1.07 CONTROL 0.06969 0.0610 Inf 1.142 0.9678
## (-1.07 CONTROL) - (-1.07 LOSS) 0.17080 0.0727 Inf 2.350 0.3122
## (-1.07 CONTROL) - 0 LOSS 0.19747 0.0425 Inf 4.646 0.0001
## (-1.07 CONTROL) - 1.07 LOSS 0.22414 0.0351 Inf 6.381 <.0001
## (-1.07 CONTROL) - (-1.07 WARM) 0.17267 0.0534 Inf 3.231 0.0337
## (-1.07 CONTROL) - 0 WARM 0.14664 0.0344 Inf 4.263 0.0007
## (-1.07 CONTROL) - 1.07 WARM 0.12062 0.0372 Inf 3.240 0.0327
## 0 CONTROL - 1.07 CONTROL 0.03484 0.0305 Inf 1.142 0.9678
## 0 CONTROL - (-1.07 LOSS) 0.13596 0.0778 Inf 1.747 0.7173
## 0 CONTROL - 0 LOSS 0.16262 0.0508 Inf 3.201 0.0370
## 0 CONTROL - 1.07 LOSS 0.18929 0.0448 Inf 4.224 0.0008
## 0 CONTROL - (-1.07 WARM) 0.13782 0.0603 Inf 2.287 0.3496
## 0 CONTROL - 0 WARM 0.11180 0.0442 Inf 2.527 0.2189
## 0 CONTROL - 1.07 WARM 0.08577 0.0465 Inf 1.845 0.6517
## 1.07 CONTROL - (-1.07 LOSS) 0.10111 0.0932 Inf 1.084 0.9766
## 1.07 CONTROL - 0 LOSS 0.12778 0.0722 Inf 1.769 0.7028
## 1.07 CONTROL - 1.07 LOSS 0.15445 0.0681 Inf 2.266 0.3628
## 1.07 CONTROL - (-1.07 WARM) 0.10298 0.0792 Inf 1.301 0.9314
## 1.07 CONTROL - 0 WARM 0.07695 0.0678 Inf 1.135 0.9689
## 1.07 CONTROL - 1.07 WARM 0.05093 0.0693 Inf 0.735 0.9983
## (-1.07 LOSS) - 0 LOSS 0.02667 0.0381 Inf 0.700 0.9988
## (-1.07 LOSS) - 1.07 LOSS 0.05334 0.0762 Inf 0.700 0.9988
## (-1.07 LOSS) - (-1.07 WARM) 0.00187 0.0844 Inf 0.022 1.0000
## (-1.07 LOSS) - 0 WARM -0.02416 0.0738 Inf -0.327 1.0000
## (-1.07 LOSS) - 1.07 WARM -0.05018 0.0752 Inf -0.668 0.9991
## 0 LOSS - 1.07 LOSS 0.02667 0.0381 Inf 0.700 0.9988
## 0 LOSS - (-1.07 WARM) -0.02480 0.0603 Inf -0.411 1.0000
## 0 LOSS - 0 WARM -0.05083 0.0444 Inf -1.146 0.9672
## 0 LOSS - 1.07 WARM -0.07685 0.0466 Inf -1.649 0.7773
## 1.07 LOSS - (-1.07 WARM) -0.05147 0.0554 Inf -0.929 0.9914
## 1.07 LOSS - 0 WARM -0.07750 0.0374 Inf -2.074 0.4910
## 1.07 LOSS - 1.07 WARM -0.10352 0.0400 Inf -2.589 0.1910
## (-1.07 WARM) - 0 WARM -0.02603 0.0306 Inf -0.850 0.9953
## (-1.07 WARM) - 1.07 WARM -0.05205 0.0613 Inf -0.850 0.9953
## 0 WARM - 1.07 WARM -0.02603 0.0306 Inf -0.850 0.9953
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: tukey method for comparing a family of 9 estimates
# interactions::interact_plot(compcond_eli_stereo, pred = eli_self_pmc, modx = itt_comp_gmc, mod2 = target_condition, interval = TRUE)
eli_stereo_int <- effect("eli_self_pmc:itt_comp_gmc:target_condition",
xlevels = list(itt_comp_gmc = c(-1.07, 0.0, 1.07),
target_condition = c("CONTROL",
"WARM",
"LOSS")),
mod = compcond_eli_stereo)
eli_stereo_int <- as.data.frame(eli_stereo_int)
eli_stereo_int$itt_comp_gmc <- as.factor(eli_stereo_int$itt_comp_gmc)
eli_stereo_int$target_condition <- as.factor(eli_stereo_int$target_condition)
eli_stereo_int %<>%
mutate(target_condition = forcats::fct_relevel(target_condition, c("CONTROL", "WARM", "LOSS")))
target_labels <- c("CONTROL" = "Control",
"WARM" = "Warm",
"LOSS" = "Loss")
ggplot(eli_stereo_int, aes(eli_self_pmc, fit, group = itt_comp_gmc)) +
geom_smooth(method = "lm",
size = .7,
se = FALSE,
colour = "black",
aes(linetype = itt_comp_gmc)) +
theme_minimal(base_size = 13) +
theme(legend.key.size = unit(1, "cm")) +
facet_wrap(~target_condition,
labeller = labeller(target_condition = target_labels)) +
scale_linetype_manual("Threat composite",
breaks = c("-1.07", "0", "1.07"),
labels = c("Low",
"Average",
"High"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Residual projection by target-level threat and target condition",
subtitle = "Using the ELI; Accounting for stereotyping",
x = "ELI responses for self",
y = "ELI responses for target")
# checking normality of conditional residuals
qqnorm(residuals(compcond_eli_stereo), main="Q-Q plot for conditional residuals")
# checking the normality of the random effects:
qqnorm(ranef(compcond_eli_stereo)$sub_id$eli_self_pmc,
main="Q-Q plot for the self random effect")
# looking at random effect for stereo:
qqnorm(ranef(compcond_eli_stereo)$sub_id$eli_stereo_pmc,
main="Q-Q plot for the self random effect")
plot_model(compcond_eli_stereo, type='diag')
## [[1]]
##
## [[2]]
## [[2]]$sub_id
##
##
## [[3]]
##
## [[4]]
This may be starting to take on a funnel shape, but not so severe that I think I need to be concerned